Radical Gravity

Spyropoulos Studio | Radical Gravity

RADICALMATERIAL GRAVITY HYBRID RESPONSIVE AIR

ADAPTIVE

Special thanks to AKT II Amin Yassin Daphne Drayiou Panagiota Tsaparikou Yuji Huang Yunyu Huang Salih Ege Savcı

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Index

01.

Studio Brief

02.

Design Thesis

19

Radical Gravity

22

Design Features

24

03.

Introduction

43

04.

Background Research

73

Flight Control Studies

75

05.

06.

07.

6

TABLE OF CONTENTS

9

Examining Air

133

Material Adaptation

159

Collective Intelligence

221

The Unit

235

Prototype 01

236

Prototype 02

248

Prototype 03

254

Prototype 04/05

258

Prototypes Evaluation

270

Pre-Deployment

273

Hybrid System

274

Precalculation of the Deployment

276

Context Specific Settlements

280

Flight Calculation

298

Deployment

309

Air Drop

312

08.

09.

10.

Forming Bodyplans

316

Unfolding Parachute

344

Manoeuvring

349

Aerodynamic Research

350

Manoeuvring Strategy

372

Landing

411

Aggregation Phase

412

Landing Phase

430

Land Operation

455

The Settlement Model

456

Self-sustainability

488

Adaptation

510

Cellular Living

526

Long-term Adaptation

534

Appendix

539

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01

Studio Brief o Studio Brief o Natural Disaster Relief o Emergency and Transitional Shelter

STUDIO BRIEF

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STUDIO BRIEF

Rapid Response for Disaster Scenarios Studio Brief

Spyropoulos Studio AA DRL

Social Ecologies, the new agenda of AA DRL,

The studio has a history constructing non-linear

considers the society an interacting system consisted

systems, that evolve through feedback. They differ

of different interdependent parts and suggests

considerably from linear ones since they do not

investigating these relationships. Crises and disasters

obey the “superposition principle”. In non-linear

are the situations from which society suffers the most,

systems interaction of the parts result in a different

they broadly affect the whole system, exposing other

and greater outcome, than a pure sum of the parts.

issues and activating the chain reaction. They threat

This can be also referred to as “Emergence” or result

human life, health, and well-being on a large scale.

of Collective intelligence. Studio search for design

They cause devastation for years and sometimes

systems exploiting these qualities for increasing

even lead to the collapse of whole governments.

resiliency and effectiveness, designing simpler units

Spyropoulos studio aims to respond to these extreme

that achieve a greater result on a collective scale.

situations with a considerable level of uncertainty and when the urgent response and architectural strategic

And last but not least, the studio is searching for

intervention is needed.

effective solutions to deal with Disaster Scenarios but primarily they should be very human. The way

These important problems of society reveal that

the architectural space is capable of affecting human

architecture and existing response fails due to its low

well-being, the complexity of architectural formations

level of flexibility and extremely slow and ineffective

that result from the interdependency of the material

response. The studio aims to address it designing

and construction systems is considered to be crucial

adaptive and generative systems rather than finite

for the design and humans.

solutions, enabling the fast and effective reaction able to deal with high numbers. The studio sees architecture as a framework that is time-based, performative, and behavioural. Design through research and research through design opens radical solutions and new ways that are not considered by conventional disaster relief architecture.

STUDIO BRIEF

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Disaster relief for Communities Affected by Natural Disasters Natural disasters is a global problem that takes place almost in every country and affects close to 160 million people worldwide. Natural Disasters include earthquakes, tsunamis, volcanic eruptions, landslides, hurricanes, floods, wildfires. They have an immediate impact on human lives and often destroy the physical, biological and social environment of the affected people, thereby having a longer-term impact on their health, well-being, and survival.

The frequency of natural disasters has increased in the last five decades, one of the reasons being climate change. On average, 31 and half million people are displaced by natural disasters every year and 2,5 million people are in need of shelter.1 Providing shelter that bridges the gap https://ourworldindata.org/ grapher/number-homeless-fromnatural-disasters 1

between disaster and reconstruction of permanent housing is essential to re-establish normalcy in such a chaotic situation. Shelter in this context is a crucial contributor to survival, security, personal safety, protection from the climate, and resistance to ill health and disease.

The problems of mass-homelessness caused by Natural Disasters are tackled by disaster relief programs that operate in phases from the disaster towards normality. First, just after the disaster event happened, it is urgent to provide Relief. In this first phase, people receive Emergency shelter, kits, NFIs, Wash assistance. The second phase after relief is called Early recovery and it involves Transitional shelter, damage assessments, rehabilitation of services. The third phase is Recovery,

“Disaster Response Shelter Catalogue,” Disaster Response Shelter Catalogue (Habitat for Humanity International, 2012), 12.

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STUDIO BRIEF

it is when the core house and infrastructure construction happens.

Finally, Institutionalization or the last phase implicates neighbourhood development and full house construction. 2

STUDIO BRIEF

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National Geophysical Data Center, “Natural Hazards Data, Images and Education,” Natural Hazards Data | NCEI (U.S. Department of Commerce, July 28, 2006), https:// www.ngdc.noaa.gov/hazard/ hazards.shtml.

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STUDIO BRIEF

STUDIO BRIEF

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Emergency and Transitional shelter solutions After an event of Natural Disaster, the first phase of

Prefab is the only option that can operate globally and

the response is to provide emergency shelter. The

fast, being both emergency and transitional shelter.

two most common types are tents and repurposing

But it has a high cost, huge problems with shipping

large existing buildings such as the sports arena for

or deployment (destroyed land infrastructure or too

accommodation.

many arrangements-negotiations slower the process

In the first case, a tent is fast and easy to assemble

considerably), extensive production time, assembly

but undoubtedly very uncomfortable and only

problems, no adaptation to any change and complete

operational for an extremely brief period. Tents are

ignorance of human well-being.

also inflexible, offer almost no protection from the climate, brittle, require constant repair and face

Overall, existing types or strategies of shelters have

deployment challenges.

different problems. They are inflexible, unstable to

In the second case, staying in existing buildings

climate, bad for ecology and lead to deforestation,

means no privacy, more chance for diseases to

disregard social norms and well-being, require

spread. It goes against social and cultural norms and

professional help and many human resources for

only possible for a very short time. “They cram them

construction, meet all kinds of transportation and

onto cots, put all your personal belongings in a plastic

assembly challenges.4

garbage bag, stick it underneath, and put you on the floor of an entire sports arena, or a gymnasium.” 3 For

Most designs have been focused on shelter as

people that just experienced Disaster this could be

a product rather than on the problem of giving

psychologically very challenging.

accommodation to a population that needs to rebuild their lives. Usually, designs tend to be fixed

The second phase provides a transitional shelter,

on ideal solutions where flexibility must be adopted.

which most common types are shelter kits, rental

Furthermore, the solutions completely ignore any

subsidies and prefab.

human well-being, aesthetic part and the fact that

Shelter kits require very long construction time,

people went through serious stress, that they tend

training, and professional help, which many times

to be shocked, traumatized, and extremely worried

is impossible to find. This solution is also extremely

about their future. The design of many shelters does

difficult to implement on a global scale and the use

do not include any source of natural light, which is not

of local resources often leads to deforestation and

only depressive, it is dangerous for the psychological

depletion of resources.

state of people that just went through trauma.

Rental Subsidies are possible in a very limited number standards, the competitive market may result in

Michael McDaniel, “Cheap, Effective Shelter for Disaster Relief,” TED, https://www.ted.com/talks/michael_mcdaniel_cheap_ effective_shelter_for_disaster_relief.

exploitation and abuse, there is a high risk of inflation

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of cases, it is very difficult to monitor if shelter meets

and speculation.

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STUDIO BRIEF

3

“Emergency Handbook,” UNHCR, https://emergency.unhcr. org/entry/57186/shelter-solutions.

Shelter Typologies

(a) Tents

(b) Existing building use

(c) Shelter kits

(d) Rental subsidies

(e) Prefab

(f) Prefab STUDIO BRIEF

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02

Design Thesis o Radical Gravity o Design Features

DESIGN THESIS

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Local Response

Global Response

Either fast or durable

Fast and durable

Land-dependant deployment

Air deployment

Slow construction by hand

Fast construction in the air

DESIGN THESIS

Fixed and finite

Adaptive and time-based

Standardised ghetto

Diverse spaces

Dependant on infrastructure

Self-sustainable

Terex-Comedil

Terex-Comedil

Terex-Comedil

Terex-Comedil

Abandoned after use

Participating in city recovery

DESIGN THESIS

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Radical Gravity GLOBAL SYSTEM THAT PROVIDES AIRDROP SHELTERS AFTER NATURAL DISASTERS Natural disasters are an urgent problem on a global scale. Their number is increasing due to varied reasons – most prominently climate change – leaving millions of people without a home.

Radical Gravity attempts to design an efficient global system of a fast and strategic response that accommodates these people in shelters, while creating environment-friendly and sustainable shelter settlements with respect to human well-being.

Following natural disasters, land infrastructure is often compromised.

We therefore propose using the high-altitude airdrop method for emergency shelters deployment. Instead of limiting the stage air dropping to the delivery of standard shelters, we consider this as a self-building or self-construction stage that distributes ready-to-use emergency shelter settlements to the ones affected.

The research searches for more passive strategies of control, such as manoeuvring the flight using air resistance through changes in the drag distribution during the aggregation level. The aggregation flight results in new architectural morphologies that differ depending on environmental and site-specific parameters.

These shelter settlements consist of inflated units that are selfsustainable and adaptive to population needs. Using passive strategies of harvesting water, managing energy transitions, and controlling the microclimate, the project aims for a circular model of operation and intelligent ecological thinking.

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DESIGN THESIS

DESIGN THESIS

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Design Features Main components

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01

02

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04

Air Infrastructure

Air Dropping for delivery

Air Dropping for construction

Chemical Infatables

The necessity to make

Air Dropping emergency

Air Dropping as a

Chemical Inflatables as an ideal material

the system global for

shelter settlements

co-creator and co-

effective operation and

implements complete

constructor of shelters.

system for Air Dropping

the fact that often after

land-independency,

The contracting airflow

and adaptive shelters

Natural Disasters the

extreme flexibility,

while dropping is a

settlements. The robotic

land infrastructure is

being the fastest global

construction force and

inflatable is controlled

compromised induce the

deployment strategy

the falling stage is a self-

through smart inflation

use of Aviation.

possible.

building stage.

with chemical reaction.

DESIGN THESIS

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Adaptive system

Self-Sustainable

Space for human well-being

Participating in city recovery

Simultaneously

Destroyed infrastructure

Losing the house is one

The whole process of

centralised and

after disasters means that

of the most important

recovery rather than

decentralised. The

settlements should be self-

primary stress factors.

designing a shelter as an

environment and

sustainable. Harvesting

The diversity of shape

object. When permanent

population’s needs

water and managing

and spaces, different

housing is rebuilt, the

actually shape the

energy transitions, the

lighting conditions, all

units (former shelters)

form and aggregation

project aims for a circular

stimulate human well-

will continue playing

logic.

model of operation and

being and help to cope

the important role in the

ecological thinking.

with trauma.

city’s life and recovery.

DESIGN THESIS

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01

Air Infrastructure

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DESIGN THESIS

Natural Disasters occur all around the planet but frequently due to political and economic reasons many countries are unable to respond adequately and on time. For this reason, we propose an independent system that is designed to operate all around the world. It is crucial to ensure that human lives and human well-being are above all: above weak economies, twisted political regimes and poorly working governmental structures.

No matter where you live, in case of emergency you will receive thoughtful and comfortable shelter as fast as possible.

The necessity to make the system global for effective operation and the fact that often after Natural Disasters the land infrastructure is compromised induce the use of Air infrastructure for deployment, specifically Aviation. Aeroplanes are the only vehicles capable of operating globally with minimal disruptions. The air network has a high level of adaptability real-time and can reach any location around the globe.

Fig 1. A global map showing areas natural disasters occur.

Individual airports to be used to link different locations.

Areas where natural disasters occur.

Fig 2. A global map showing airport network.

DESIGN THESIS

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02

Air Dropping as a Delivery Method

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DESIGN THESIS

The practice of using Air Dropping after disaster

it is impossible to land, or very distant and poorly

events for relief is rare. Mostly it is a deployment

developed regions with no airports around. In these

of necessities or other humanitarian packages,

conditions, the airdropping has no alternatives.

but never accommodation. However, in several cases, Air Dropping is the only possible way

Air Dropping emergency shelter settlements

of emergency response delivery. These cases

implements complete land-independency, extreme

include such circumstances as destroyed land

flexibility, being the fastest global deployment strategy

infrastructure after Natural Disaster, war areas, when

possible.

DESIGN THESIS

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03

Air Dropping as a Construction Method

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DESIGN THESIS

The decision to use Air Dropping as a delivery

and creates many additional problems on the way.

method requires a fresh, creative and radical

Our general premise is to use the contracting

approach to design. Architecture has to correspond

airflow while dropping as a construction force,

to the deployment method, transforming its

approaching the falling stage as a self-building stage.

challenges to architectural or design opportunities.

Radical Gravity project treats Air Dropping as

After being dropped in high altitude, the aggregation

a co-creator and co-constructor of shelters.

starts to unwrap itself through designed interaction with airflow. The unfolding lead to the parachute logic

One of the existing shelter’s problems is assembly. It

of slowing the free fall. Aggregations result in different

usually requires a workforce (sometimes professional)

formations due to the air channelling, free-fall control

and time, which delays the use of a shelter solution

and the environmental parameters.

DESIGN THESIS

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04

Chemical Inflatable as a material system

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DESIGN THESIS

The Air Dropping deployment method dictates

Air can be used also as an actuator, it is crucial for

the use of a material system that is light and can

our project to regulate the Air Dropping during the

be tightly packed. The best-fitting solution is the

deployment phase, where the central objective is a

inflatable because of the incredible difference in

weight change, and then adapt to other parameters on

volume between packed or deflatable state and

land, changing morphology for better performance.

inflated one. Inflatable structures proved to be

However, inflation and deflation require a lot of heavy

relatively cheap, reliable, quickly constructed, able

machinery that is not suitable for Air Dropping.

to cover needed for shelters spans. Furthermore,

The chemical reaction for inflation is a much lighter

they can be climate resistant, stand in severe

alternative. The reaction we use consists of water,

conditions or even during seismic activities.

citric acid crystals and baking soda, elements that are cheap, light and easy to find all around the world, which means easily replaceable. The reaction is nontoxic, effective for big volumes and produces C02 that inflates the units.

Deflated Unit

Inhabitable Unit

Chemical Inflation

DESIGN THESIS

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05

Adaptive System

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DESIGN THESIS

Overall system.

generating aggregation solutions that take into

The control system that is simultaneously

account the climate and urban development logic.

centralised and decentralised manages to be both

The units have the capacity to respond to the

adaptive and controlled. The design of the Radical

environment using sensors and computer vision.

Gravity system is hybridised and depend on the

Computer vision includes landcover classification,

phase.

object recognition, safe landing estimation, etc. The system controls free fall by drag distribution.

Deployment.

The Radical Gravity project approaches the

Land Operation.

environment not as passive surrounding but an

After landing, the system adapts to population

actual driver of change, the one that actually

needs, changing the urban arrangement, water

shapes the form and aggregation logic. The

harvesting logic and micro climate.

system reacts and adapts to airflow, terrain, and unpredicted malfunction of specific units,

Topography

Surroundings

Air Flow

DESIGN THESIS

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06

Self-sustainable

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DESIGN THESIS

After Natural Disasters there is a big chance that

Climate Control.

infrastructure is compromised so the settlements must

Transitional shelters needed to be safe and comfortable.

be self-sustainable. This self-sustainability can be

The climate control tries to use the already existing

acquired through the collection of such necessities for

elements of the system and aggregation logic. The

survival as water and manage energy transitions, aiming

heating happens by distributing heat from the kitchens.

for a circular model of operation or ecological thinking.

For this reason, in a cold climate, the units aggregate in a closed airtight surface to preserve as much heat

Through different disciplines architecture

as possible. Kitchen areas with public gatherings are

“deterritorializes” itself acquiring new approaches

dispersed around the settlement to enable this strategy.

and a more holistic view, it “constitutes and extends”

The cooling happens by using chemical inflation.

the territory itself. One of these disciplines is

Because the chemical reaction we use to inflate the air

thermodynamics, the branch of physical science that

pockets of the units is endothermic (it absorbs heat from

deals with the relations between heat and other forms

the atmosphere) we control the temperature inside of the

of energy (such as mechanical, electrical, or chemical

aggregation by continuously inflating columns and skin.

energy). The law of Conservation of Energy states that

Another strategy is in the logic of aggregating, which is

energy cannot be created or destroyed - it can only be

creating inner courtyards to enable the wind flow.

needs.

Apart from using energy transitions or thermodynamic

transferred from one type to another. And the project

intends to utilize it for architectural and survival, living Water collection and harvesting.

principles for self-sustainability, the designed settlements

Energy Generation and Distribution. need a way to collect and harvest water for drinking,

During the Air Dropping phase, the generators inside of bathrooms and for the chemical reaction. Collected

pillars generate electric energy by falling and contracting rainwater, dehumidifiers from living pods and fog catcher

the airflow. Essentially the potential gravitational nets provide drinking water and water for other needs.

energy is converted to kinetic by dropping and kinetic These needs include showering and enabling chemical

is converted to electric with the help of a turbine and

capsules for inflation.

generator. The generated electric energy enables the

system to control the localization during free fall. After

the shelters land, the stick that holds the turbine can be

extended and the electric energy will be generated by winds and rains.

DESIGN THESIS

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07

Space for Human Well-being

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DESIGN THESIS

House is one of the most important needs for people

The Radical Gravity project provides various spaces

and essential for their well-being, providing conditions

with different degree of privacy and encourages

to live with protection, security, comfort, and

interaction, by uniting living pods (small private units)

privacy. Thus, losing a house represents more than

in the bigger structure (the Shed). The Shed creates

physical deprivation, it is losing dignity, identity and

social networks and strengths the community, which

privacy. After a disaster, people tend to be shocked,

is crucial for people’s recovery.

traumatized, and extremely worried about their future due to the losses of relatives and friends, and also

The architectural diversity of shape and spaces, which

because of the losses of their goods and belongings.

results primarily from the Air Dropping construction

Losing the house is one of the most important primary

stage opposes traditional shelter settlements

stress factors. Since reconstruction works often last

monotony, which proved to have a negative impact

long, temporary accommodation fills that period of

on the way people feel. Different lighting conditions,

time providing solutions that support vital functions

a variety of spaces, all stimulate human well-being.

such as climate protection, basic needs, security,

Adaptive qualities of the system provide the ability

privacy and comfort.1

to choose, decreasing the despair and hopelessness people usually feel after losing their homes. The self-

A shelter is officially defined as a habitable covered

sustainable features of the settlement and generated

living space providing a secure and healthy living

urban model with different functions ensures a certain

environment with privacy and dignity. Everyone

level of comfort, that stimulates the recovery and the

has the right to adequate shelter, space to live

transition to normality.

and store belongings as well as privacy, comfort and emotional support. 2 Many architectural prefab solutions completely ignore this aspect, that space of temporary shelter needs to help people to recover.

Daniel Félix et al., “The Role of Temporary Accommodation Buildings for Post-Disaster Housing Reconstruction,” Journal of Housing and the Built Environment 30, no. 4 (2014): pp. 683699, https://doi.org/10.1007/s10901-014-9431-4, 4.

1

UNHCR, “Shelter Design Catalogue,” Shelter Design Catalogue (Geneva: UNHCR Shelter and Settlement Section., 2016), 5.

2

DESIGN THESIS

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08

Long-term City Recovery

40

DESIGN THESIS

Many existing temporary accommodation

The Radical Gravity project approaches the whole

strategies have experienced problems with the

process of recovery, starting with the deployment,

use of the units that are no longer needed, which

then the land operation and the use of the shelters

leads to an impressive waste of resources. This

after, when permanent housing is rebuilt. In this

thoughtless approach adds to the burning waste

last stage, the units (former shelters) will continue

problem which in turn contributes to climate

playing the important role in the city’s life and

change and in the end results in a bigger number

recovery. Because usually, the reconstruction of the

of natural disasters.

city happens in phases and housing is rebuilt in the first phase, some of the former shelters will change

Dealing with such complex issues such as disaster

the function to public buildings, providing the

relief, architecture requires a holistic approach to

space for typologies that are missing. Other units

the design. Specifically, thinking about the life cycle

will become objects of infrastructure, greenhouses,

or a process rather than an object.

park pavilions, and sources of alternative energy sources and water in the city.

DESIGN THESIS

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03

Introduction

INTRODUCTION

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Air Drop

Aviation Aviation is one of the most “global” industries. Connecting people, cultures and businesses across continents, it is rapidly expanding.

Weathering crises, it demonstrated long-term resilience, becoming an

“Aviation Benefits: a Better Future,” Uniting Aviation, July 11, 2018, https://www.unitingaviation. com/news/economic-development/ aviation-benefits-for-a-betterfuture/.

1

indispensable means of transport.1 Aviation is the most suitable delivery strategy for emergency shelters because of its autonomy, real-time diversification when needed and land-independency (land infrastructure can be affected or destroyed after Natural Disasters).

Examining Civil Aviation Control systems, the air corridors are made according to Performance Based Navigation strategy completely changed the field, radically optimising it. The paths are shaped due to environmental parameters such as jet streams and other aeroplanes in the network. PBN provides complete freedom from ground-based systems, making the aeroplanes the fastest and most efficient global transport.

Figure 1. Air Network

Figure 2. PBN

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AIR DROP

Air Dropping Air Dropping is the fastest and the most efficient way of emergency shelters deployment that are delivered by aeroplanes. The practice of Air Dropping is mostly used by military purposes and space industry, however rarely it delivers humanitarian needs as well.

Figure 3. Four Sherpas shown exiting C-17A aircraft (left) and just prior to landing (right)

In the military, Air Dropping is divided into Low Altitude Dropping and High Altitude. Low Altitude Air dropping has less machinery for the precision but the use is also limited. For the high altitude dropping, there are several strategies that deal with accuracy such as the Joint Precision Airdrop System. It makes Air Dropping possible from such altitudes as 3000-2500 feet. The JPADS generally uses a round parachute, parafoil, or a parafoil/round parachute hybrid for deceleration of the load through descent. The controlled parachute provides JPADS with directional capability in flight. Often other parachutes are also utilized in the overall system for final load recovery.

The parachute control line(s) run to the airborne guidance unit (AGU) and are used to control the shape of the parachute/parafoil for directional control. One primary difference between each category of deceleration technology, i.e. type of parachute, is the horizontal achievable offset each type of system can deliver. In very generic terms, offset is often measured in terms of the systems lift to drag ratio (L/D) in zero winds. 2

Benney, R.J.; Krainski, W.J.; Onckelinx, P.; Delwarde, C.; Mueller, L.; Vallance, M. (2006) NATO Precision Airdrop Initiatives and Modeling and Simulation Needs. In Fluid Dynamics of Personnel and Equipment Precision Delivery from Military Platforms (pp. KN2-1 – KN2-22). Meeting Proceedings RTO-MP-AVT-133, Keynote 2. Neuilly-sur-Seine, France: RTO.

2

INTRODUCTION

45

Air Drop

Flight Control of different aerial vehicles Research on flight control of different vehicles brings an understanding of how directionality of flight can be manipulated. In the aeroplane, lift is mainly produced by wings or particularly by the airfoil, crosssectional shape of a wing. There are two main explanations of how the airfoil’s shape produces lift. First is based on the downward deflection of the flow (Newton’s laws), and the second is based on air pressure differences (Bernoulli’s principle). Parameters for producing lift resulted from these two phenomenons, are the speed + the “angle of attack”.

The manoeuvring happens through manipulating control surfaces: Elevators for Pitch, Rudder for Yaw, Ailerons for Roll.

A drone relies on rotors (propeller attached to a motor) for all manoeuvring, meaning the downward thrust of the drone is equal to the gravitational pull working against it. For moving forward and backwards the rotors of the drone must apply thrust while making sure the spin of the rotors keeps the drone balanced. The helicopter produces lift due to its main rotor. To change the direction of flight the swash plates tilt to a certain direction because rotor blades are dependant on the swash plates, the rotor blades are pitched lower in the front of the rotor assembly than behind it. Then the angle of attack is increased and it causes the unbalanced lift that move helicopter further for instance.

Natural models The aerial vehicles bring a certain understanding of active strategies to control the directionality of flight or fall. However, more passive strategies are more interesting, strategies without motors, that use a very small energetic resource to manoeuvre. Natural dispersal models, in particular wind dispersal ones, showcase interesting approaches for deceleration.

Gliders operate in a similar way as aeroplanes do, having specific aerodynamic wings that help them to glide or to stay on the air longer.

This design is comprised of a specific distribution of weight and surface area that creates equilibrium between symmetric parts. The maple seeds have completely different logic of descending. Because of their “Blowing In The Wind,” Wind Dispersal Of Seeds, https://www2. palomar.edu/users/warmstrong/ plfeb99.htm.

3

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AIR DROP

specific design, they start to auto-rotate, which is how they produce lift. Dandelions have a logic of a very interesting type of parachute that descends not due to air resistance but a vortex ring that is produced because of the porous top. 3

Figure 4. Flight control of airplane, drone and helicopter.

Figure 5. Wind Dispersal

INTRODUCTION

47

Air Drop

Figure 6. Radical Gravity. Materiality of Air physical experiments

Figure 7. Radical Gravity. Producing lift through spinning logic, weight distribution tests

Figure 8. Radical Gravity. Directing airflow - angle of attack tests

48

AIR DROP

Controlling Free Fall What proved to be crucial for free fall control is the way the object meets fluid or Air resistance. From a passive understanding of air, we shift to active, the actual driver of change.

In general, parameters of Freefall can be divided into two categories: time or z coordinate and position or directionality of fall. One of the ways to control time or speed of falling is a drag – sort of fluid friction, acting opposite to the relative motion of the object. It can be manipulated by the surface area and the way the object meets fluid, its shape. The strategies we found to control it is porosity (surface area), shape and rigidity distribution (because it provokes different shapes and angle of attack).

Lift is opposite to drag, but it also results in a slower speed of falling.

The strategy we researched is auto-rotation (based on maple seeds behaviour), which can be initialized by specific weight distribution.

In terms of the directionality of falling – the centre of gravity plays a major role, which can be controlled by changing the weight distribution.

An alternative strategy to regulate positioning is to direct airflow, forming the interaction between fluid masses inside, which can be done by shape change and angle of attack variation.

Figure 9. Radical Gravity. Controlling free fall parameters

INTRODUCTION

49

Air Drop

Air Deployment in Architecture Buckminster Fuller advocates that in the age of global connectivity, radio, space, and all technologies that are dramatically changing the life, architecture also has to be global. He argued for the independence of architecture from the land in order to escape from the concept of the property towards more internationally shared principles of conduct.

He saw houses more as a part of the network rather than integral with the land.

In his project 4D Tower (1928), towers are dropped in any place by zeppelin being “as free of land statics as a boat”. The large radio antenna in the top of the structure links the building between themselves and with systems of the movement. Architecture is becoming a network rather than an object. Fuller promoted his design in mid-1928 with a manifesto

4

Mark Wigley and R. Buckminster Fuller, Buckminster Fuller Inc.: Architecture in the Age of Radio (Zurich: Lars Müller Publishers, 2015), 22-28.

that advocates for the new networking dynamism of “International telephone, telegraphic photography, and national radio ‘hook-ups’ from four corners of a continent, to all of a continent.” He also approaches the physical networking industries of aeroplane, railway, and automobile as being “like a giant broadcasting system” in need of an upgrade.4

Figure 10. 4D Tower by Buckminster Fuller (1928)

50

AIR DROP

Figure 11. 4D Tower deployment by Buckminster Fuller (1928)

The similar idea of network architecture, completely independant from land is Instant City by Archigram (late 1960s-early 1970s).

It is a concept of a temporary city that can be set up overnight, consisted of a transportable kit of parts that can be quickly assembled to provide the inhabitants of small towns with access to the resources and cultural attractions of a large metropolis.

Figure 12. Instant City by Archigram (late 1960s-early 1970s)

INTRODUCTION

51

Air Drop “We started to speculate, instead of a city trundling around on trucks, maybe it could all hang off an airship and it would just silently come in Benedict Hobson, “Archigram’s Instant City Concept Enables ‘a Village to Become a Kind of City for a Week’ Says Peter Cook,” Dezeen, May 13, 2020, https://www.dezeen. com/2020/05/13/archigram-instantcity-peter-cook-video-interviewvdf/.

5

the night and you’d open the bedroom curtains and there was the city in the field behind you,” said Cook. 5 In-between permanent and transitory, mobile and ephemeral, Instant City embodies the vision of an aerial city that transforms the surrounding into a reactive environment. Architecture is a framework that plugs into the existing urban fabric.

This view of architecture as a global system or network, that deploys instantaneous cities is particularly useful for emergency shelters settlements. Land independence is crucial for deployment in areas affected by Natural Disasters when infrastructure is often destroyed.

Radical Gravity uses Air Dropping of shelters delivered by aeroplanes because it is the fastest global strategy of deployment possible.

Figure 13. Radical Gravity. Air Deployment.

52

AIR DROP

Figure 14. Mars Competition by Foster and partners (2015)

Foster + Partners / www. fosterandpartners.com, “Mars Habitat: Foster + Partners,” Architecture Projects | Foster + Partners, https://www. fosterandpartners.com/projects/ mars-habitat/.

6

The example of using specifically Air Dropping in architecture can be seen in Mars Competition by Foster and partners (2015). Operating in another extreme environment, the habitat is supposed to be delivered to Mars in two stages prior to the arrival of the astronauts. First, the semi-autonomous robots select the site and dig a 1.5-metre deep crater, followed by a second delivery of the inflatable modules by Air Dropping.6

INTRODUCTION

53

Air Drop

Air dropping for construction In Radical Gravity the Air dropping is not only a pure delivery or deployment method of predefined shelters, but also a tool to construct or a source of actuation. The architectural form results from an interaction between different parameters, some of them partly predefined and some of them are changing real-time. The way the dropped shelters interact with airflow and with each other in order to navigate results in different landscapes depending on the situation.

The behaviour of the system can be explained in the example of the formation of “epigenetic landscape”.

Figure 15. a.Epigenetic landcape from above b.Epigenetic landscape from below

“Epigenesis” is the term that describes how form emerges gradually but dynamically. For a long time, it was believed that the way genes result in individual traits such as eye or hair colour is linear and we are no more than an assembled mosaic constituting representing no more than a sum of the parts. The radical shift happened when the scientific community discovered that the individual gene affects a wide range of physical characteristics and they are more anchors or triggers in a complex dynamic system rather than a set of specific instructions.

The ball in the epigenetic landscape from above (Fig 15a) depicts a cell in an early developing embryo. The general pathway of the ball is predetermined but “its specific pathway can only be determined through real-time events, depending on environmental conditions, selection pressures, and perturbations encountered along the way.” 7 7 Sanford Kwinter, “Soft systems” in Culture lab 1, ed. Brian Boignon (New York: Princeton Architectural Press, 1996), 217.

54

AIR DROP

Epigenetic landscape from below (Fig 15b) reveals the intricate system of chemical interactions that determine at a distance its topographical features. The space of continuous interaction that forms the landscape.

“Each guy rope represents a chemical tendency produced by a gene, but only rarely do these influences make direct contact with the epigenetic surface; their influences are always mediated by their interaction with many other chemicals and genes, and every point in the system is at least somewhat sensitive to any change or event anywhere else in the network. No action in any single gene can Gail to affect, or be regulated by, all the other genes in its functional complex. If entire regions are blown out, the landscape will undergo deformation, but even this won’t prevent the systems inbuilt flexible strategies.”

8

Kwinter, Soft Systems, 217-218

8

The same way the epigenetic landscape is formed by the interaction of “genes”, the formation of the Radical Gravity architecture is developed through the interaction with airflow, coming from parachute logic and air channelling. The aggregation of units results from interaction between some predefined parameters as climate and other parameters that change real-time such as wind, terrain or unpredicted malfunction of specific units. The manoeuvring by changing the drag distribution is another driver for change in aggregation formation.

This interdependent relationship between environmental parameters and architectural elements in construction has been also explored in the Water Tower by Buckminster Fuller (1959). The elastic surface acts

Figure 16. Radical Gravity.

Construction in the air.

as an observable input so that the participants either surrounding or making use of this particular architecture have the chance to get visual feedback. Such ingenious ways to communicate with parameters allows surrounding participants (manmade or not) to take informed decisions so that when we look at this relationship from a bigger scale we can see interactive communities which are all dependent for each others’ input.

Allowing the building blocks of epigenetic organisms to find faster and more efficient ways to communicate between themselves makes the overall system more resilient, increasing the amount or the flexibility of possible outcomes.

In the end, when combined with tensile fabric a surface was able to stretch or deform, bearing different capabilities of weight throughout the implemented surface. The amount which this depicted amount of volume would undergo change heavily depends on the elastic potential of the chosen material and the strategies where overall geometry guides nonregular input. Consequently, the architectural landscape emerges out of communication between the surrounding and architectural elements.

Figure 17. Water Tower, Buckminster Fuller (1959)

INTRODUCTION

55

Chemical Inflatable

Working on a deployable system, the ratio between packed state and unpacked one has crucial importance. The suitable material has to be somehow universal and appropriate for Air Dropping. Air is the material that perfectly responds to these requirements. Air is the best material for emergency shelter solutions because it is fast, cheap and does not need a professional workforce for construction.

Air can be an architectural material with amazing structural capabilities.

Pressurized air beams and other types of inflatable structures proved to be relatively cheap, reliable, quickly constructed, able to cover huge spans. Air structures are climate resistant, can stand in severe conditions or even during seismic activities.

For example, Fuji Pavilion, built in Japan in 1970 and designed by Murata and Kawaguchi was one of the first air-inflated structures that covers a large span. 50m in diameter, the structure was formed of 16 tubular arches, each with a cross-sectional diameter of 4m. Balancing compressed force of air and the tensile force of membrane, the structure

William McLean and Pete Silver, Air Structures (London: Laurence King Publishing, 2015), ?.

9

is able to cope with varying external forces, increasing the internal pressure from 7.8 kPa to 24.5 kPa to resist storm conditions.9 In this way structure can be seen as a step from static construction to dynamic.

Figure 18. Fuji Pavilion by Murata and Kawaguchi, 1970.

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CHEMICAL INFLATBLE

Air opens possibilities of change in structure or adaptation to unforeseen circumstances, using it as an actuator. We want to exploit these advantages of air to produce a smart, optimized, and eco-conscious emergency shelter solution. The robotic manipulation of air is the concept exploited by soft robotics. The field is relatively new but one of the most promising in robotics. Working with compliant materials is highly logical in situations when a robot has a direct interaction with humans or weight is an important parameter.

Figure 19. Soft Robotics

Soft robotics in architecture has its own history, comprised of mostly experimental architectural studios work.

Yellow Heart by Haus-Rucker-Co is an installation demonstrated in 1968.

Exploring the perception of space, they designed a capsule consisting of

“Yellow Heart,” Architectuul, http://architectuul.com/ architecture/yellow-heart.

10

three air rings and inner sphere. Being inside of the sphere visitors could experience the expansion and the contraction of space by manipulating the air inside of “pillows”. 10

Figure 20. Yellow Heart by Haus-Rucker-Co, 1968 INTRODUCTION

57

Chemical Inflatable

The same year Prada Poole constructed a prototype of La Casa Jonas.

The pneumatic cellular structure has a capacity to change changing the air pressure in the wall segments that could be elongated or reduced.

The idea was to construct a house that can respond to the use and wishes of its occupants.11

May 2nd Wednesday and April 8th Sunday, “Pneumatic Shelter Archives,” cyberneticzoo.com, http://cyberneticzoo.com/tag/ pneumatic-shelter/.

11

Figure 21. La Casa Jonas by Prada Poole, 1968.

One of the most recent examples of active air structure is The Nordic Pavillion inflatables by Eero Lunden, 2018. The Nordic Pavilion has been filled with huge blobs that slowly inflate and deflate based on atmospheric and environmental conditions.

Figure 22. The Nordic Pavillion inflatables by Eero Lunden, 2018.

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CHEMICAL INFLATBLE

They have sensors that monitor the surrounding carbon dioxide levels, humidity and temperature. The cells “breathe” in response to these environmental conditions, depending on the carbon-dioxide levels left behind by humans, and change colour to indicate temperature differences.12 So comparing to previous examples, here the architecture initiated the interaction with the environment and humans, acquiring its own sensing capacity.

Jessica Mairs, “Huge Balloons Inflate and Deflate Based on Atmospheric Conditions inside Nordic Pavilion,” Dezeen, May 25, 2018, https://www.dezeen. com/2018/05/25/nordic-pavilionvenice-architecture-biennaleinflatable/.

12

The manipulation of air is crucial for our project in order to regulate the air dropping and then adapt to other parameters on land, changing morphology for better performance. However, inflation and deflation require a lot of heavy machinery that is not suitable for Air Dropping, which is our deployment method. Searching for lighter alternatives, the use of chemical reaction for inflation appeared as best suited in our situation.

In MIT Media Lab the Tangible Media Group explored a simple reaction between baking soda, citric acid and water for medical and packaging needs. The reaction is safe, non-toxic and results in CO2, inflating the air bag. The air pressure can be controlled through the ratio of sodium bicarbonate and citric acid and the amount of water in the reaction, while the final pressure is solely dependent on the concentration of citric acid. For a 0.1L bag, 0.35g of sodium bicarbonate and 0.8g of acid were used to obtain a pressure of 1 atmosphere or 101 kPa. 13

P. Webb, V. Sumini, A. Golan, and H. Ishii, “Auto-Inflatables.”

In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems CHI EA 19 (2019), LBW1411.

13

Figure 23. Auto Inflatables by the Tangible Media Group MIT Media Lab, 2019

INTRODUCTION

59

Chemical Inflatable

Conducting the physical experiment to assess this chemical reaction we also realised that it can be a driver to weight manipulation for manoeuvring during deployment.

Figure 24. Radical Gravity. Chemical Inflation physical experiment.

The system that regulates the chemical reaction we designed operates combining chemical capsules in dissolvable packages with water. The opening and closing parts of the tube regulate the specific air pocket that inflates. The manipulation of air through this designed “chemical computer system” exploit these advantages of robotic inflatable to produce a smart, optimized and eco-conscious emergency shelter solution.

Figure 25. Radical Gravity. “Chemical Computer system”.

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CHEMICAL INFLATBLE

INTRODUCTION

61

Collective Intelligence

Collective Intelligence or emergence is a process when larger entities arise through interactions among smaller or simpler entities that by themselves do not exhibit such properties. One of the good examples of Collective Intelligence in nature is the ant colony. Ants do not follow the instruction on a global scale but everything they perform individually or locally is for “the greater good”. The swarm behaviour of the colony is exhibited through limited simple instructions each ant follows. The reaction of each individual triggers a change in the collective.

Figure 26. Ant Colony.

There are three very powerful qualities similar to ant’s behaviour that are crucial for our research. First is that the interaction between collective result in simpler and cheaper units, which is important for affordable emergency shelters. Acting collaboratively they can achieve very sophisticated tasks through their interaction. Second is that if some malfunction appear and units cannot contribute to the global task, the system will reach the goal anyway, without losing them. The third is the energy optimisation: the system can distribute work, initiating activity of specific units while others remain passive.

The project The Distributed Flight Array developed in ETH Zurich in 2010 explores the dynamic flight model combining simple units with one rotor and developing strategies for flight control of different aggregations.

Following the general logic of quadrotor, the units achieve a controlled flight as a group, balancing the forces for stabilization. This is achieved through strategically assigning certain units to rotate clockwise while others counter-clockwise.14

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COLLECTIVE INTELLIGENCE

14 Raymond Oung and Raffaello D’Andrea, “The Distributed Flight Array,” Mechatronics 21, no. 6 (2011): pp. 908917, https://doi.org/10.1016/j. mechatronics.2010.08.003.

Figure 27. The Distributed Flight Array, ETH Zurich, 2010

In Radical Gravity project during deployment units collectively adapt changing the drag distribution for controlling the directionality of fall.

By changing it in a certain way, the fall of the aggregation tend to move in corresponded trajectory. In other words, it controls a course of the flight. The collective way to approach the problem allows performing this complex task of manoeuvring after dropping with simpler and cheaper units, making the process more resilient and energy saving.

After deployment, in the operation stage, units collectively distribute the resources and change the formation to increase the performance.

Figure 28. Radical Gravity. Collective Control of free fall.

INTRODUCTION

63

Adaptive System

The hybridised control system that is simultaneously centralised and decentralised, manages to be both adaptive and controlled.

The Boolean Network Experiment, conducted by Stuart Kauffman and others at the Santa Fe Institute for the Study of Complexity showcased a critical point between the “gas state” highly dynamic and uncontrollable state and “solid” – highly ordered and controlled.

In this experiment, standard Boolean networks were set up. Each sell has an “on” or “off” state – black or white. Every element linked with others know their states and switches itself based on the information about neighbours. The experiment showed that the system’s movement eventually stops and the system gets “stuck”. It mostly becomes grey which means no longer capable of any activity. However, in certain arrangements of units, the system is becoming “liquid” or in between – it has highly ordered parts that maintain a general structure and it has a continuous flow. 15

15 Sanford Kwinter, “Soft systems” in Culture lab 1, ed. Brian Boignon (New York: Princeton Architectural Press, 1996), 219-222

Figure 29. Boolean Network Experiment by Stuart Kauffman.

This experiment, translated to the system’s design language reveal the general promise of a combination of a centralised and decentralised control strategy. The centralised systems are highly ordered and controlled but they are non-resilient and non-adaptive (frozen, have no movement). Decentralised systems showcase high adaptation through self-organisation but for important operations that happen in the limited time they appear extremely disordered and uncontrollable. So the design of Radical Gravity system is hybridised and depend on the phase.

Adaptive control requires the ability to learn, being responsive to parameters that can vary or uncertain ones. Adaptive control always

64

ADAPTIVE SYSTEM

requires some sort of feedback, mostly maintaining a closed-loop structure. In our project units need to adapt to the environment and their location during deployment and environment and human needs during land operation.

The adaptation to the environment started to be tested computationally in Alife field. In 1994 Karl Sims presented his research Evolving Virtual Creatures, a computational framework that managed to generate creatures consisted of primitives depending on the environment. Each creature was placed in a specific environment or task in a simulated threedimensional world with Newtonian physics. Karl Sims had a hundred creatures simulated in a generation and they all were evaluated how well they fit for a particular problem. The variety came from mutations each generation had. Eventually, they tended to evolve towards results that indeed can be observed in nature (a snake for a swimming group for example). 16 16 Rodney A. Brooks, Flesh and Machines How Robots Will Change Us (New York, NY: Vintage Books, 2003), 181-183.

Figure 30. Evolving Virtual Creatures by Karl Sims, 1994.

INTRODUCTION

65

Adaptive System

The Radical Gravity project also approaches the environment not as passive surrounding but a driver of change, the one that actually shapes the form and aggregation logic. The system reacts and adapts to airflow, terrain, and unpredicted malfunction of specific units generating aggregation solutions that take into account the climate, urban development.

The adaptiveness is inseparable from the ability of a system to sense the environment and its own structure and position. One of the ways to acquire information for the units is to use computer vision or specifically place recognition, land cover classification, object recognition, safe landing estimation, etc.

Figure 31. Preliminary site selection. Emergency Landing Algorithm. Civil Aviation Control Systems

The understanding of aggregation structure and malfunction of particular units can be done through continuous self-modelling. Resilient machines are one of the topics investigated by Evolutionary Robotics field. The most well-known and influential representative of the field is Josh Bongard.

He argues that robots need a robust performance under uncertainty.

Bongard draws attention to the problem that in case of unexpected

66

ADAPTIVE SYSTEM

damage most machines fail, which showcase a low level of robustness and adaptiveness. In his project Resilient Machines, developed with Victor Zykov and Hod Lipson, the robot proved to recover from damage or loss of its parts through continuous self-modelling. This robot called Starfish is a four-legged robot that has two joints with motors in each leg, joint angle sensors in each joint and two tilt sensors in the main body, sensing how much it tilts in left-right and front-back directions.

Starfish uses a physics engine to evolve controllers in simulation with the help of evolutionary algorithms first and then examines some of those in the real world, continuously updating the physics engine. When the robot has damage or loss of its parts, the simulation stops matching the reality. For this reason, the robot is forced to adapt the simulation to reflect these changes, evolving a new controller. After the loss of its leg, Starfish was able to construct new models that were accurate enough to produce compensating behaviours, enabling it to move forward.17

17 J. Bongard, V. Zykov, and H. Lipson, “Resilient Machines Through Continuous SelfModeling,” Science 314, no. 5802 (2006), https://doi.org/10.1126/ science.1133687, 1118-1121.

Figure 32. Bongard, J., V. Zykov, and H. Lipson.

“Resilient Machines Through Continuous Self Modelling”, 2006 INTRODUCTION

67

SelfSustainable

Water Collection Being self-sustainable is crucial for emergency shelters settlements and water is one of the first necessities that can be difficult to find in certain parts of the world.

In 1972 Graham Stevens created Desert Cloud installation to demonstrate the way to condense the water from the atmosphere. The pneumatic structure, composed of a clear polyester film, inflates and rises due to heated air by the sun. The water condenses on the inner walls of the

William McLean and Pete Silver, Air Structures (London: Laurence King Publishing, 2015), ?.

18

inflatable, thereby providing a means of obtaining a vital resource in the desert.18

Figure 33. Desert Cloud by Graham Stevens, 1972.

Figure 34. Warka Tower by Arturo Vittorio, 2015.

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SELF-SUSTAINABLE

In 2015 Arturo Vittorio demonstrates his project Warka Tower, which main goal is to serve communities providing drinking water. Warka tower collets rainwater and harvests water from air with Fog Catchers net. Air always contains a certain amount of water vapour, irrespective of local ambient temperatures and humidity conditions. This makes it possible to produce water from the air almost anywhere in the world. 19

“Warka Tower,” Warka Water, https://www.warkawater.org/warkatower/.

19

In Radical Gravity project, the settlements collect and harvest water for drinking, bathrooms and for the chemical reaction. Using a similar strategy as Desert Cloud and Warka Tower, the units harvest water from air with fog catcher net. Water from air, collected rainwater, dehumidifiers provide drinking water for a settlement and water for other needs.

Figure 35. Radical Gravity. Water Collection.

Thermodynamics The first law of thermodynamics is the law of conservation of energy.

The energy can be neither created nor destroyed. But because the total energy of an isolated system is constant, energy can be transformed from one another. There are different types of energy and in Radical Gravity project we use 5 of them: Gravitational potential energy, chemical, movement or kinetic energy, electrical and thermal one (heat). The general attempt is to utilize gravitational potential energy first, acquired from dropping, and then manage to create a circular system that creates living conditions in a passive way.

Potential

Kinetic

Stored Mechanical Nuclear Chemical Gravitational

Electrical

Energy Types

Light Heat Kinetic

Figure 36. Energy Types.

INTRODUCTION

69

SelfSustainable

One of the uses can be climate control of the settlement. The most famous project that speculated on climate control in large scale is The Dome over Manhattan by Buckminster Fuller, 1960. The general idea was to have a controlled temperature under the Dome and as he calculated, it would reduce the heat loss eighty times, comparing to the standard model.

Figure 37. The Dome Over Manhattan by Buckminster Fuller, 1960.

Then In 1971, Frei Otto presented the project for a city of 40.000 inhabitants to be built in the Arctic circle beneath a pneumatic dome of 2 km in diameter. The energy was going to be produced by a nuclear power station which would have also provided air heating and harbour’s water pumping in order to maintain sub-icing conditions. The dome consisted of a double-layered transparent foil retained by a steel cable net, pressurized air provided the structural support of the dome.

Figure 38. The Arctic City by Frei Otto, 1971.

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SELF-SUSTAINABLE

In Radical Gravity project, climate control tries to utilize already existing elements of the system and aggregation logic. The overall strategy is similar to the Dome over Manhattan and the Arctic City in terms of using the larger volume for climate control (the Shed, where all living pods are placed) The heating happens by distributing heat from the kitchens. For this reason, in a cold climate, the units aggregate in a closed airtight surface to preserve as much heat as possible. Kitchen areas with public gatherings are dispersed around the settlement to enable this strategy.

The cooling is managed by mainly using chemical inflation. Because the chemical reaction we use to inflate the air pockets of the units is endothermic (it absorbs heat from the atmosphere) we control the temperature inside of the aggregation by continuously inflating columns and skin. Another strategy is in the logic of aggregating, which is creating inner courtyards to enable the wind flow.

Figure 39. Radical Gravity. Climate Control.

INTRODUCTION

71

04

Background Research o Flight Control Studies *

Civil Aviation Control Systems

*

Aerial Vehicles

*

Natural Models

*

Dispersal Studies

*

Man-made Airdrop

*

AirDrop Control

o Examining Air *

Materiality of Air

*

Air as Catalyst

*

Air Architecture

*

Air Structures

o Material Adaptation *

Inflatable-Deflatable hybrid

*

Artificial Muscles

*

Stitching

*

Combining different rigidity levels

o Collective Intelligence *

Swarm Behaviour

*

Interaction Rule-sets studies

RESEARCH

73

74

FLIGHT CONTROL STUDIES

o Flight Control Studies *

Civil Aviation Control Systems

*

Aerial Vehicles

*

Natural Models

*

Dispersal Studies

*

Man-made Airdrop

*

AirDrop Control

o Examining Air *

Materiality of Air

*

Air as Catalyst

*

Air Architecture

*

Air Structures

o Material Adaptation *

Inflatable-Deflatable hybrid

*

Artificial Muscles

*

Stitching

*

Combining different rigidity levels

o Collective Intelligence *

Swarm Behaviour

*

Interaction Rule-sets studies

75

Control Systems Research Civil Aviation Ecology We examined control systems of Civil Aviation to use aerial networks for deployment and to understand the existing models of controlling the high population of changes for our population-driven system. We started from the small scale of these control systems or mechanical ones (airplane), then we examined the control systems of the airport or medium-scale (operational) and control system - traffic management of the airspace or large scale. In all these cases the main vulnerability of these systems is a dependency on human interference.

Environment Jet Streams, headwind/tailwind, wind Shear, turbulence, low level jet winds, microbursts, crosswind, icing, birds, aerial vehicles, heavy rain, hailstorm, low cloud cover, sand and dust storms

Airplane Loads, maneuver limits, wing system, engine and engine control, control surfaces Airplane control system: Sensors, autopilot flight control, interface, dangerous cases protection

Airport Maintenance facilities and equipment, fueling capacities, planning facilities, people and cargo

Airspace Radars, rdios, instrument landing system, control computers and displays

Human factors Flight crew: Awareness and attention, adherence to procedures, embedded skills, coordination of activities, fatigue, training Maintenance and Dispatch: Adherence to procedures, quality of work, training, level of optimization Air Traffic Control: Awareness and attention, adherence to procedures, fatigue, training, level of optimization

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FLIGHT CONTROL STUDIES

S

Airplane

M

Airport

L

77 Airspace

BACKGROUND RESEARCH

Airplane Design aspects

Image 1: Diagram of main forces acting during the flight

Centre of pressure

Centre of gravity

Image 1

Main Logic

the balance between forces and counterforces.

The most basic aspect of aircraft’s design and performance is to keep

Lift Lift in the aircraft is mainly produced by wings or particularly by airfoil, cross-sectional shape of a wing.

There are two main explanations of how the airfoil’s shape produces lift. First is based on downward deflection of the flow (Newton’s laws), Airfoil

and second is based on air pressure differences (Bernoulli’s principle).

Deflection of the flow is possible because of the Coanda effect - the

Image 2. Airfoil

tendency of a fluid jet to stay attached to a convex surface.

Parameters for producing lift, resulted from these two phenomenons, are the speed + the “angle of attack”. Angle of attack is the angle between the airfoil’s reference line and the oncoming flow.

To increase the amount of lift, extending the airfoil, such contol surfaces as flaps and slats are used. Aerodynamic optimization of airplane’s geometrical shape helps to generate additional small amount of lift.

Furthermore, the control of the opposite force - weight, by decreasing Image 3. Evolution of the airfoil

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FLIGHT CONTROL STUDIES

it allows to reduce the needed amount of lift.

Thrust Thrust is generated by some kind of propulsion system. Most modern airliners use turbofan engines because of their high thrust and good fuel efficiency. The basic logic is that the engine compresses the air, mixes fuel with it, ignites the fuel/air mixture, and shoots it out the back of the engine, creating thrust.

Drag Drag is the opposite force of thrust and generally is produced by every part of the airplane because of the difference in velocity between the solid object and the fluid. However, for specific situation such as landing, aircraft’s design provides spoilers (control surfaces).

Image 4. The diagram of a turbofan working

Manoeuvering Control surfaces also regulate manoeuvering in all 3 axes. Elevators for Pitch, Rudder for Yaw, Ailerons for Roll.

Pitch

Yaw

Roll

Image 5: Control surfaces of the airplane.

Image 5

BACKGROUND RESEARCH

79

Control System

Permission to take off

Thrust

Lift

Altitude

Engine

Slats; Flats

Elevators

Thrust

Lift

Altitude

Manoeuvring

Engine

Slats; Flats

Elevators

Elevators; Rudder; Ailerons

Thrust

Manoeuvring

Engine

Elevators; Rudder; Ailerons

Thrust

Altitude

Manoeuvring

Engine

Elevators

Elevators; Rudder; Ailerons

Permission to land

Thrust

Engine

80

Drag

Altitude

Image 2. Overall diagram

Image 1. Phases of flight. Diagram of usual operations and used controlled parameters

Manoeuvring

Elevators; Spoilers, Elevators Rudder; engine FLIGHT CONTROL STUDIES Ailerons reversed

Sensors Sensors, embedded in the airplane’s design are mostly monitoring the state of elements (not the environment) such as control surfaces, engine, landing gear and brakes or cockpit controllers (for example brake pedals)

Cabin, galley and cargo

Engine, turbine and APU Flight controls and actuation

Cockpit controls Landing gear and brakes

Image 3

Image 3. Sensors of the airplane.

Flight controls and actuation: position of control surfaces.

Autopilot Autoflight of Airbus is done by FMGC systems (Flight Management Guidance computers).

Cockpit controls: autopilot disconnect force sensors, sensors of cokpit elements like brake pedal

Engine, turbine and APU: Accelerometers, thrust reverser position - engine

Image 4

Image 4. Work of FMGC system.

In general, FMGC has a Flight Management and a Flight Guidance part.

The Flight Management part: •

Navigation – the accurate position of the aircraft and the capability to automatically follow the programmed flight

•

Flight Planning – the flight plan computation

•

Performance optimization – cost, speed and altitude optimization

•

Predictions – the accurate estimates for waypoints, altitudes, speeds, fuel and arrival times

•

Display Management – the control of information to display autoflight modes and navigation info

The Flight Guidance part: •

Commands to the flight control computers to control pitch, roll and yaw

•

Autothrust commands to automatically control thrust

•

Flight Director commands to the pilot fo control of pitch, roll and yaw

BACKGROUND RESEARCH

81

OPTIMIZATION

Jet Streams

Environment

Headwind/Tailwind

the phenomenons can be used to optimize the route, landing or taking

Environmental factor is crucial in the airplane’s performance. Some of off. However, most of them are dangerous and can cause destabilization, TURBULENCE

failure of parts or difficulty in keeping control of the plane.

Wind Shear (CAT) Thermal Turbulence Mechanical Turbulence

Dangerous Cases Protection The airplane’s control system has the dangerous cases protection, that takes the control from the pilot and stabilizes the plane in several dangerous sitations.

Wing Trace Turbulence Low level jet winds

There are : High Angle of Attack Protection, Load Factor Protection, High Pitch Attitude Protection, High Speed Protection, Bank Angle Protection.

Most of them are related to the angle of attack (of airfoil). Because of the high angle, the coanda effect (mentioned in the design aspect part)

DESTABILIZATION

Microbursts

stops following the shape of the airfoil and this can cause stall, which is very dangerous.

Crosswind steady flow FAILURE OF PARTS

stall point

Icing

separation point

loss of lift

Birds

separated flow

Drones

Image 2. The angle of attack.

The dangerous case protection takes the controlling from the pilot when the angle of attack is close to the separation point (automatically disabing any pilot’s actions) and stabilizes the airplane.

Interface

Other aircraft

Many instruments are combined in one to concentrate the important LOW VISIBILITY

Heavy rain Hailstorm Low cloud cover

information in one area.

Primary flight display: speed scale, heading/track, VSI- the vertical speed indicator, altimeter, flight mode, compass heading Navigation display: GPS navigator. Clareshield: control of automation.

MCDU: The pilot can enter the speed of the flight, the heading, the altitude and the vertical speed.

Sand and Dust storms

Image 1. Environmental circumstances grouped by their affect to the aircraft.

Image 3. Clareshield.

Image 4.

Primary flight display (left) MCDU (middle), Navigation display (right)

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FLIGHT CONTROL STUDIES

Optimization The current control system is highly dependant on the human factor and has no interaction with the environment. In case of an unpredicted change everything depends on the human response, which increases risks.

Computer vision For optimization, the aircraft needs to acquire certain level of sensing.

Airbus’ Autonomous Taxi, Take-Off & Landing (ATTOL) project uses a combination of image recognition and flight control computer modifications to perform a competely autonomous take off.

Image 5

Image 5. Airbus demonstrates first fully automatic vision-based take-off that was demonstrated on 18 December 2019 (ATTOL)

Adaptive flight controller Adaptive controllers provide a feedback mechanism for modifying their parameters to improve in situ the control system performance, without sacrificing robustness. The controller adapts based on the actual response of the physical system operating in its actual environment.

One of the most interesting, realistic and robust architectures for airpane’s flight controller is MIAC model (Model Identification Adaptive Control). MIAC is an example of indirect adaptive control, in which the adaptive mechanism provides estimated system parameters to a separate process that calculates the controller parameters.

Image 6

Image 6. MIAC. Adaptive Control System Architectures. Verification of Adaptive Systems. U.S. Department of Transportation Federal Aviation Administration.

Final Report. April 2016.

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Air — Land

Emergency Landing Algorithms To facilitate the potential use of Unmanned Aerial Vehicles in civil applications, forced landing technologies in an emergency case are recognized as a major enabling technology that will allow the integration of UAVs in the civilian airspace. To achieve an equivalent level of safety to manned aircraft, the emergency landing technology is mainly comprised of two steps: landing site selection and trajectory planning and tracking to decide flight routes or patterns.

1. Machine Vision Landing Site Selection The landing sites are chosen based on their size, shape and slope as well as their surface type classification. Subsequent algorithms have been developed that allow automatic classification of the candidate landing site’s surface. The aim for the selection of candidate landing site is to locate regions from aerial imagery that were of similar texture, free of obstacles and large enough to land in.

To achieve that, the candidate landing site output process is comprised of four essential layers of input information as the Image 1 indicates: 1.

Preliminary site selection based on the analysis of size and shape of the surface. 2. Surface type classification. 3. Slope mapping. 4. Additional information like wind data. This chapter would mainly describe about Image 1. Candidate landing site selection architecture

algorithms used in the preliminary selection which is responsible to choose “where” in detail.

Edge Detection Measure The Edge Detection measure analyzes information of surrounding areas to mainly find areas that are of similar texture and free of objects.

Firstly, the Canny edge detector (Canny, 1986) observes clear borders between different regions in the image by the edge gradient map. (Image 2) A line expansion algorithm immediately follows the edge detection to involve the examination of the pixels of all edges found. For each pixel found, the algorithm inspects the surrounding pixels within a certain search radius. If another edge pixel is found, the algorithm will set all pixels within this radius to a “1”. (Image 3) It ensures a suitable boundary is placed between detected obstacles and potentially safe Image 2. Edge gradient map of test image by Canny Edge Detection

areas to land in. The search radius size in this algorithm can be altered depending on the UAV’s height above ground level, to maintain this suitable safety zone.

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Image 3. Line Expansion Algorithm

Image 4. Edge detection measure output for a number of test images

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Intensity Measure The Intensity Measure is required to help distinguish between manmade and natural objects in the aerial images.

It was observed through experimentation that man-made regions in the images often exhibited the highest intensity values in the image. These objects included roof tops, cars and sheds - anything that had a high reflectance. On the contrary, natural surfaces such as grass and trees generally were of lower intensity.

The measure is formulated by dividing the intensity image space into 10 equal partitions and labelling any pixel in the highest valued intensity region a 1 and the other lower intensity valued pixels a value of 0. Any pixel with a value of 1 was considered to be a man-made region and assigned a very unsafe value which would not be considered as a potential UAV forced landing site. Man-made objects are indicated in white and natural surfaces in black in the following test images.

Image 5. Intensity measure result for a number of test images 1: Man-made objects, indicated in white 0: Natural surfaces, indicated in black

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Geometry Acceptance The Geometry Acceptance phase then takes this map and looks for areas of appropriate size and shape for a UAV forced landing that contain pixels with values only equal to “0”. This phase locates landing areas of a given size and shape suitable for candidate landing sites. (Image 6)

Decision Making: Preliminary Site Selection The masks are individually moved over the output map from the combination of the edge detection and intensity measure described in the previous section. The image area that the mask passes over is scanned to decide whether or not the area contains entirely safe pixels.

If all pixels in the area are safe, then the area is marked as a candidate.

Image 6. Landing site matrix mask definitions including four masks: A-D The masks are rectangular in shape and scalable (size is determined by the pixel resolution calculation) The masks are rotated in a number of orientations, catering for approaches to the candidate site from different directions

Image 7. Preliminary site selection output map examples

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2. Trajectory Planning and Tracking To achieve advanced flight control systems without a human, this section addreses how to navigate to land on a chosen site in unknown terrain, while taking into account the operational flight envelope of the UAV and dynamic environmental factors such as crosswinds and gusts, small flying objects and other obstacles.

Image 8

Wind Compensation In the current forced landing simulationm, the initial wind velocities are given by uniformly distributed random numbers that are updated every sixty seconds. These numbers generate the initial, Wnorth , Weast, Wdown components, which are then multiplied by a continuous square wave giving the profile shown in Image 8.

Image 9

Correction for wind is performed using the principles of vector algebra

Image 8. Wind components WN: Green, WE: Pink, WD: Blue

to compute the wind correction angle, which is compared with the

These components are used to compute the resultant wind vector incident on the UAV

current aircraft heading and passed as input to the UAV flight planning

Image 9. Wind triangle calculations

opposite direction, so that the “track made good” will converge on the

subsystem. (Image 9) For example, it implies that the wind correction angle supplied to the flight planning subsystem must be 15° in the “required track” to target.

Decision Making: Flight Path Planning To improve upon the algorithm of the wind compensation, a remodelled path planning, tracking and control strategy have been implemented that does not restrict the robotic aircraft to following trajectories developed for human pilots.

In the initial step, the path planning algorithm selects the waypoints for the UAV to navigate in an appropriate circuit pattern with a degree Image 10

Image 10. Forced landing circuit patterns

of pitch attitude. (Image 10) And then, the system remodels the path planning under changing wind values. (Image 11) Given a desired start and end position, the shortest path to the goal can be constructed geometrically in the xy-plane by the union of an arc

HK: high key

of circumference. To translate the path into 3-D, the portions of path

LK: low key

described by the arcs are then transformed into that traced segments of

EB: end base DH: decision height

helix, and the two segments are then joined together by a straight line. In

OSI: overshoot 1

the Image 12, the desired initial and final positions are indicated by red

AP: aimpoint

arrows, and these positions are linked by the shortest path highlighted in red.

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Image 11.Detailed view of the forced landing simulations under changing wind.

Green arrows indicate the direction of the wind.

Image 12. The planned descent trajectory in 2-D. The optimal path is shown in red, joining the desired start and end positions. (red arrows)

Image 12

Image 13

Image 13. The planned descent trajectory in 3-D.

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Trajectory Tracking Lastly, to control the flight path and trajectory in real time, the trajectory tracking strategy is introduced. Essentially, the guidance logic selects a reference point on the desired trajectory, and then generates a lateral acceleration command using the reference point.

In the tracking calculation process, it includes the airspeed of the aircraft, the distance to the reference point, forward of the vehicle, and the Image 14. Diagram for guidance logic (Park, 2007)

angle between the airspeed and the distance vectors. The angle vector controls the direction of acceleration to follow an instantaneous circular segment with radius in the Image 14. Under the process, it outputs the trajectory tracking pattern to control unmanned landing flight path.

(Image 15-16)

Image 15. 2-D view of the flight path using the path planning and trajectory tracking algorithms.

Wind is indicated by the green arrows.

Image 16. 3-D view of the same flight path.

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91

Airport Air Travel Data Main Logic Airports are key articulation points in the circulatory system of the global economy. They mediate flows of people and goods. The importance of an airport in this regard is a function of its centrality and its intermediacy. Air refers to a node’s role as an origin and destination gateway to a surrounding region, and Port refers to the degree to which a node serves as an interchange between different regions.

Image 1. Total number of movements, PAX and cargo in weigth.

Image 2. Heathrow Terminal 1-5

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Image 1.

Image 2.

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Heathrow Traffic Statistics January 2005 - March 2020 Passengers

Airport Transport Movements + Cargo In Metric Tonnes January 2005 - March 2020

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Airport Taxonomies Airport Taxonomies Parameters taken into consideration: Walking Distances Passenger Orientation Baggage Systems Cost Duplication of Terminal Facilities and Amenities Transfer Bag Logistics Flexibility of Terminal for Future Changes Comparability with Future Aircraft Design

Abu Dhabi. AUH

Transporter New Delhi. DEL

Doha. DOH

Istanbul. IST

Dubai. DWC

London. HTR

Jeddah. JED

Pier

Satellite

Linear

Compact Module

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Atlanta. ATL

Dallas. DFW

Los Angeles.LAS

Chicago. ORD

Amsterdam. AMS

Frankfurt. FRA

Helsinki. HEL

Vienna. VIE

Hong Kong. HKG

Kansai. KIX

Beijing. PEK

Singapore. SIN BACKGROUND RESEARCH

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Airport Systems Airport site location involves a wide variety of considerations, which consists of; 1. Air transportation forecast demand 2. Runway configuration 3. Altitude 4. Meteorological conditions 5. Topography 6. Environmental considerations 7. Adjacent land uses 8. Local accessibility 9. Obstructions 10. Adjacent airports Image 3. Effect of breakdown and delay on apron dispatch.

Airports as an Ecology The airport operators vary significantly in relation to the ownership, management structure, funding and degree of autonomy, thus making the manner with which one airport is managed to be significantly different from the other. Nevertheless, each airport operator is faced with challenging tasks of coordinating all services to enable the efficient functionality of the airport system. Each airport operator has a unique responsibility, but all assume the overall responsibility for control and coordination of the operations of the airport.

Image 4. Working shifts and personell layout algorithm diagram.

Image 5. Total delay caused by year in minutes

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Image 5.

Relationship Diagram

AIRSIDE

DEPARTURE

APPROACH

RUNWAY

RUNWAY WAREHOUSE TAXIWAY

FUELER

FUEL DEPOT

TAXIWAY

PUSHBACK OPERATIONS

LUGGAGE WAGONS

APRON

APRON

CATERING CARTS GATE JETWAY

GATE JETWAY

PIER’ BUS

DEPLANING STAIRS

CATERING

ARRIVAL CONCOURSE

DEPLANING STAIRS

AIRCRAFT

LUGGAGE WAGONS

PASSPORT CONTROL

STORED LUGGAGE

FOOD PREPARATIONS MASS DISHWASHING

PIER

LUGGAGE TRANSFER

TRAY&DIRTY DISH COLLECTION E-PASSPORT CONTROL

BOARDING DESK

BUS

CARGO PROCESSING

CART LOADING

BAGGAGE HANDLING

CUSTOMS SECURITY TRANSFERS LUGGAGE

PASSENGER& BAGGAGE RECLAIM

FIVE STEP SCANNING MAX SECURITY SCANNING

DEPARTING CURBSIDE CATERING RETAIL LOUNGE

SERVICE ENTRY

TRANSFERS LUGGAGE

SECURITY BORDER CONTROL

TERMINAL

CHECK-IN COUNTER

ARRIVING CURBSIDE

AIRPORT ENTRY & EXIT

RAILWAY

PARKING

ROADS

OTHER GROUND TRANSPORT

ROADS

PARKING

Operational Human Luggage Staff

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99

Airport Automation of Logistics

Image 6. Computer vision system for optimal luggage placement.

Image 7. Cart loading optimization.

Image 8. Automated transfers juncture belt.

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Detection of the range of working enviroment.

Using computer vision to sort luggage of different size.

Airport logistics systems inAirport logistics systems in-

clude air cargo management

clude air cargo management

and baggage handling sys-

and baggage handling systems

tems at airports. Global air

at airports. Global air cargo traf-

cargo traffic is increasing

fic is increasing significantly,

significantly, and there is an

and there is an increasing need

increasing need to develop

to develop more specific cargo

more specific cargo terminals

terminals to meet increasing

to meet increasing global air

global air cargo traffic.

cargo traffic.

Image 9.-10 Automated transfers luggage and early-bag storage system.

Large airports around the world are used for activities related to customs and heavy freight transport. Private cargo terminals must meet international security standards and effectively manage air cargo activities and invest substantially in efficient and next-generation cargo management systems.

Large airports around the world are used for activities related to customs and heavy freight transport. Private cargo terminals must meet international security standards and effectively manage air cargo activities and invest substantially in efficient and next-generation cargo management systems.

Image 11-12. Vanderlade luggage systems automation solution, mobile luggage belts.

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Flights Airports are large facilities with hundreds of complex systems that must operate in the most efficient manner possible to minimize costs while providing the high level of service expected by passengers and visitors.

Many of those systems incorporate software, data-base, and network components. They generate both on-demand and regular reports to assist airport management, improve operations and reduce cost at the same time. In order to be more effective and efficient in their operations, they need to understand the competitive advantage of these systems and how to align them together in a better way to serve the people.

Image 3. Passenger Services timeslot sheet.

Image 3.

Flights: Timetable, which includes name of the airline, type of aircraft, numbers of arrival and departure flights, Scheduled Time of Arrival (STA), Scheduled Time of Departure (STD) and the number of gate, where the aircraft will be serviced during its turnaround.

Workers: Identification number of worker, his qualification, tasks that he is qualified to perform and his working hours.

Gates: Type of the gate (finger or remote) and the time needed to move from any gate to any other.

Image 3. A.O.D.B. Diagram

Service Level Agreement: SLA defines strictly the order, time and duration of handling services delivered to the aircrafts.

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Relationship Diagram

GALLEY SERVICE TRUCK (1st POSITION)

PASSENGER BRIDGE

ELECTRIC POWER

BULK CARGO LOADER E BULK CARGO TRAIN

STARTING AIR SEQUENCE 1

AIR CONDITIONING

AC

FUELING

BULK CARGO TRAIN VACUUM LAVATORY SERVICE

BULK CARGO LOADER CABIN CLEARING TRUCK GALLEY SERVICE TRUCK (2nd POSITION)

POTABLE WATER TRUCK

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Air Network

1. Area navigation or RNAV

Performance Based Navigation

Aircraft fly more direct and optimized paths, and air traffic controllers can block smaller amounts of airspace.

2. Required navigation performance or RNP It is even more precise, the aircraft is contained within a narrow corridor and the procedures can be designed within pinpoint predictable curved paths that provide even greater operational opportunities.

Phases 1. Departure: 10,000 RNAV flights now depart every week because aircraft are no longer restricted to fly over the ground. Systems and navigation precision greatly increased, more departure exits or onramps to fly into on-route airspace. Expedite aircraft into overhead streams efficiences. reducing fuel time and emission aircraft.

2.En- Route: Aircraft transitioning to and from airports, because PBN provides freedom from the ground based systems, therefore, expanding the limited en route road system into a highway system, by adding routes where otherwise not possible.. they were also able to fly optimized direct routes, or multi-lane highway to serve multiple airports and connect high volume metroplexes in the arrival phase as flights near their destination. finally, they could have more exits or off-ramps enabled.

Image 1. Displaying various optimization systems : Conventional routes, RNAV and RNP.

Image 2. RNP performance based navigation at various phases: Take off, Departure, Cruise, Metering descent, Final approach and landing.

Performance Based Navigation System

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3. Approach phase Procedures can be designed to allow the aircraft to descend efficiently by reducing or eliminating the need for level flights, otherwise called OPD (Optimized profile descent), works by improving how the aircraft flies from higher altitudes down to the runway, therefore reducing engine noise and saving gallons of fuel aircraft, as well as reducing carbon emission.

Image 3. Displaying tunneling route during the departure phase.

Image 2. Comparison between air traffic control in RNP, RNAV and the traditional approach.

Image 4. Diagram displaying optimization descent strategy.

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Aerial Connectivity on a Global Scale In order to respond to natural disasters rapidly on a global scale, making use of already existing air-travel infrastructure is essential.

Connections between airports around the world.

Differentiated by airlines.

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Decentralization Assuming the fastest possible travel medium is air, specific locations of existing air travel infrastructure becomes secondary. The relationship behind global connectivity would be key when coreographing an immediate response which would require a custom ratio of goods to be sent to a specific location seperately.

Airports ranked by their weight of connectivity.

A total of 72,000 daily connections inbetween nodes.

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Once the connectivity density on the geographically accurate positions airports and edges between these nodes have been defined, the behavior of the entire graph under these sources may then be simulated as if it were a physical system.

In such a simulation, the forces are applied to the nodes, pulling them closer together or pushing them further apart depending on the number of arrival and departure in-between specific nodes.

This is repeated iteratively until the system comes to a mechanical equilibrium state. The final positions of the nodes in this state represent a map of total global connectivity in a day through airports, differentiated with types of airlines.

BACKGROUND RESEARCH

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Relaxed Connectivity Diagram with Host City Labels.

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Beijing, CHINA

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Atlanta, U.S.A.

Istanbul, TURKEY

Paris, FRANCE

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Aerial Vehicles Drone Drone To explore possibilities that can bring the use of unmanned vehicles to our project we decided to use a drone - RTF Quadcopter -AR Drone Parrot 2.0 Parrot

ROS Robotic Operational System Ros is a set of software libraries and tools for robotic applications. We decided to investigate the abilities of a drone with its help because of a powerful open-source library of ROS projects, links to libraries with neural networks, openCV libraries and others, good simulation abilities and a possibility to establish a link with a real robot.

Autonomous Control We want to look at autonomous control algorithms such as a proportionalintegral-derivative controller (PID Controller) - a closed-loop control based on feedback mechanism for autonomous flights.

Ultrasound receiver, Altimeter, Accelerometer, Gyroscope, Magnetometer

Bottom vertical 60 FPS videocamera

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Rechargeable LiPo battery, processor, Wi-Fi chipset, USB port,

Frontal HD Videocamera of 720p, with wide-angle lens (92° diagonally)

15 W brushless motors (28,000 rotations per minute) with controller

Helicopter spin the shaft, the wings start to develop lift. A

Lift Lift is created by airfoil similar to airplane, by deflecting air downward and benefiting from the equal and opposite reaction that results.

A rotary motion is the easiest way to keep a wing continuously moving.

helicopter rotor system is the combination of several rotary wings (rotor blades) and a control system that generates the aerodynamic lift force that supports the weight of the helicopter, and the thrust that counteracts aerodynamic drag in forward flight. Each main rotor is mounted on a vertical mast over the top of the helicopter, as

Helicopter Main Rotor The helicopter’s rotating wing assembly is normally called the main rotor. If you give the main rotor wings a slight angle of attack on the shaft and

opposed to a helicopter tail rotor, which connects through a combination of drive shaft(s) and gearboxes along the tail boom.

Forward Motion

Reverse Motion

LIFT

LIFT THRUST

THRUST

AIR

AIR WEIGHT

WEIGHT

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Natural Models Gliders Gliders include seeds with two lateral wings that resemble the wings of an airplane. They become airborne when released from their fruit and sail through the air like a true glider. One of the best examples of this method is Alsomitra macrocarpa, each packed with hundreds of winged seeds. The seeds have two papery, membranous wings, with Image 1. The remarkable winged seed of the tropical Asian climbing gourd Alsomitra macrocarpa.

The entire seed has a wingspan of 5 inches (13 cm) and is capable of gliding through the air of the rain forest in wide circles. This seed reportedly inspired the design of early aircraft and gliders.

combined wingspans of up to 5 inches (13 cm).

Parachutes Parachutes include seeds or achenes (one-seeded fruits) with an elevated, umbrella-like crown of intricately-branched hairs at the top, often produced in globose heads or puff-like clusters. The slightest gust of wind catches the elaborate crown of plumose hairs, raising and propelling the seed into the air like a parachute.

Image 2. Parachutes in a dandelion.

Helicopters Helicopters include seeds or one-seeded fruits with a rigid or membranous wing at one end. The wing typically has a slight pitch like a propeller, causing the seed to spin as it falls. Depending on the wind velocity and distance above the ground, helicopter seeds can be carried considerable distances away from the parent plant. The spinning Image 3. Helicopters

action is similar to auto-rotation in helicopters, when a helicopter “slowly” descends after a power loss.

Flutterer / Spinners Although this type of dispersal is similar to single-winged helicopter seeds, the flutterer/spinners include seeds with a papery wing around the entire seed or at each end. Whether they spin or merely flutter depends on the size, shape and pitch of the wings, and the wind velocity.

Image 4. Flutter/ spinenrs

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Cottony Seeds & Fruits Cottony seeds and fruits include seeds and minute seed capsules with a tuft (coma) of cottony hairs at one end, or seeds embedded in a cottony mass. Some of the examples in this group are very similar in function to parachute seeds, but probably are not carried as far by the wind.

Tumbleweed Tumbleweeds are a type of stiff, sharp-pointed, awl-shaped seeds. They

Image 5. Cottony seeds & fruits

often pile up in wind rows along fences and buildings. As they roll along hillsides and valleys, the seeds are scartered across the landscape and create a mass.

Miscellaneous This miscellaneous category of wind-blown seeds and fruits includes

Image 6. Tumbleweed

plants that really don’t fit the above 6 categories. Their flower stalks fragment into seed-bearing spikelets that blow into the wind.

Flight of a Falling Seed The flight pattern of a falling seed differs from the quiescent condition and the wind condition. In the initial state, it falls without rotation until

Image 7. Miscellaneous

the transition period, while showing free fall. After the point, the falling pattern is effected by the wind condition.

Auto Rotation After a certain transition period, it goes through auto-rotation. The flight path of the seed varies in the wind condition. The rotation pattern depends on the centre of mass, the length of the seed, the proportion or the form of the wing of the seed. The decending velocity and the rotating velocity change while falling.

Image 8. Simulation of the flight of a falling maple seed, (Injae Lee and Haecheon Choi, 2016)

BACKGROUND RESEARCH

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Natural Models Producing Lift

Gliders

Maple Seeds

Parachutes

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Dispersal Active Dispersal It involves organisms that are capable of movement under their own energy.

Passive Dispersal The organisms have evolved dispersal units, or propagules, that use the kinetic energy of the environment for movement.

Parameters From the research in the nature system, the following parameters could

Image 1. Disperal Vector.

be derived: centre of gravity, wing design(scale, length, and proportion), and the type of dispersal vector(active or passive). Those parameters could be applied in a cell scale. However, if they are applied to the collective scale, types of the group and how the passive and active vectors are distributed would be the main parameters.

Cell

Collective

Image 2. Parameters in the cell scale.

Image 3. Parameters in the collective scale.

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Dispersal Studies Packing density

We started to examine Low Altitude Dispersal by ‘Explosion’ and its relationship with packing pattern, density and cluster formation on ground.

In these cases packages are three dimensional and explode from its core.

By changing the magnitude of the pushing force we could control the location of landing.

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BACKGROUND RESEARCH

121

Dispersal Studies Packing density

Then we decided that a more two dimensional surface logic is closer to a packing logic and examined the explosion of energy release from the corners.

They formed different topologies and different organizations.

So we assume that by changing the packing pattern, and density; the magnitude of pushing force has a possibility to generate different types of organizations that can be connected with population needs or architectural or urban driven decisions.

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Dispersal Studies Types of Air-Land Transitions

We have 3 possible models of air-land transition, how units

On Ground Dispersal

reach the ground and organize: First way is that they are dropped collectively, in high population, they

reach

the

ground

connected and then disperse on land.

Low Altitude Dispersal strategy is having certain forces that push units out of a dense package, releasing each unit closer to the target in Low Altitude.

High Altitude is basically having small clusters that already reach

Low Altitude Dispersal

the land in a very controlled way and do not require additional force for organization.

High Altitude Dispersal

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Inactivated Activated

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125

Man-Made Airdrop Parachutes Parachute A parachute is a device used to slow the motion of an object through an atmosphere by creating drag (or in the case of ram-air parachutes, aerodynamic lift). When the parachute is opened the force of air resistance becomes much greater than the force of gravity.

The net force on the descending skydiver has an acceleration that points upward - negative acceleration or deceleration. Eventually, the object’s weight is balanced by the air resistance. There is no resultant Image 1. Array of parachutes, designed for NASA’s Orion spacecraf. Credits: NASA

force and the object reaches a steady speed – this is known as the terminal velocity.

Paraglider Technically, they are ascending parachutes. The main difference from the parachute is in its usage, typically longer flights that can last all day and hundreds of kilometres in some cases. Controlling a paraglider happens through pulling strings, the controls you hold in your hand connect to the trailing edge of the wing. If you want to turn to the right, pull on the right control and release pressure on the left. Pulling on the controls makes the glider fly slower. Releasing pressure makes it fly faster.

Image 2. Paraglider. Credits: Gin Gliders

AIR

AIR Image 3. Paraglider maneuvering

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FORCE

THRUST

Types of Airdrop Low-Velocity Airdrop It is the delivery of a load involving parachutes that are designed to slow down the load as much as possible to ensure it impacts the ground with minimal force. This type of airdrop is used for delicate equipment and larger items such as vehicles.

Image 4. Airdrop

High-Velocity Airdrop

military transport airplane drops humanitarian aid load, dealing with the aftermath of the 2010 Haiti earthquake

It is the delivery of a load involving a parachute meant to stabilize its fall.

The parachute will slow the load to some degree but not to the extent of a Low-Velocity airdrop as High-Velocity airdrops are used for durable items like military ready-to-eat meals.

Free Fall Airdrop It is an airdrop with no parachute at all. A common example of this type of airdrop is the delivery of leaflets used in psychological warfare.

Parameters To design a control system of airdrop of a responsive shelter in the future step, the following parameters are extracted based on a springmass system for fabric surface. Each parameter shows the dynamic evolution of parachute canopy and risers.

Tensile-stiffness / angular-stiffness The geometrical deformation of a fabric surface is the major source of stress on the material. The stress causes surface tension in parachute canopy and exerts a normal component of force which the parachute canopy interacts with the surrounding fluid to produce drag of the deceleration.

Image 5. The Spring-Mass Model for Fabric Surface

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127

Rigidity of the Body Rigidity of the body brings about different vorticity around the unit. When air flows around the parachutist or cargo, the vorticity and turbulence of the flow will have different patterns that with different rigidity.

Image 6. Parachutists and vorticity near parachute with cargo considered as a mass point and a rigid body

Parachute Breathing The breathing motion is an oscillatory motion. The canopy appeared to expel the excess air by means of the breathing. In the experiment, the breathing motion was also caused by the constraint on the parachute, imposed by the guide wire. The breathing motion in the simulation is smaller because there is no vertical motion restriction such as guide wire.

Image 7. The left plot shows the parachute breathing motion.

The right plot compares the drag force measured in simulation and experiments.

Porosity The different level of porosity can lead to different patterns of inflation.

The Image 8 shows the comparison of flow pattern between fabric with low porosity and high porosity. As the comparison indicates, different level of permeability brings about different values of vorticity magnitude.

Image 8. Comparison of flow pattern between fabric with low porosity and high porosity.

The left: a canopy with high porosity The right: a canopy with low porosity.

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The parachute system is more stable with high porosity canopy but will have smaller drag force.

Collision Handling Resolution of collision between different parts of fabric surface or between soft and rigid body is also a delicate parameter in mathematical algorithm and computational geometry.

In order to resolve fabric collision during parachute inflation process, the simulation has implemented a collision handling function to detect and unwrap the surface in each time step. This method can guarantee no dynamic self-interference of cloth after a successful call of the function.

The algorithm can efficiently and robustly handle the fabric-fabric and fabric-rigid body collision.

Image 9. Fabric collision with different objects.

Parallelization and Multi-parachute System The parachute code is parallelized on both CPU and GPU system.

It adopts a hybrid parallel algorithm which solves the fluid equation through domain decomposition using MPI and the spring-mass system (an ODE system) through using GPU processors. The software is also capable of simulating multi-parachute system.

Image 10. Parallelization in 16 processors and simulation of multiparachute inflation

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129

Airdrop Control Air Resistance

During free fall what actually matters is Air Resistance. Air resistance happens when an object moves through the air.

Air resistance depends on velocity, area, and shape of the object going through the air. Altitude, temperature, and humidity change air density and, consequently, its resistance. The higher the speed and the bigger the area, the higher the resistance.

Acceleration throughout a fall gets less than gravity (g) because air resistance affects the movement of the falling object by slowing it down. How much it slows the object down depends on the surface area of the object and its speed.

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Image 1. Bullet and Air Resistance.

Credits: Rochester Institute of Technology

BACKGROUND RESEARCH

131

o Flight Control Studies *

Civil Aviation Control Systems

*

Aerial Vehicles

*

Natural Models

*

Dispersal Studies

*

Man-made Airdrop

*

AirDrop Control

o Examining Air *

Materiality of Air

*

Air as Catalyst

*

Air Architecture

*

Air Structures

o Material Adaptation *

Inflatable-Deflatable hybrid

*

Artificial Muscles

*

Stitching

*

Combining different rigidity levels

o Collective Intelligence *

Swarm Behaviour

*

Interaction Rule-sets studies

Materiality of Air Air Occupies Space We did an experiment that proves that air is a matter demonstratng that it actually occupies space. Because the bottle has an opening, the balloon can be inflated; but when an additional hole is blocked it preserves its volume and balloon doesn’t come back to its initial state until the hole is open again.

Experiment n.1 Volumetric Air Pressure

Air Flow Air as a material has fluid-like properties. In this experiment we tested series of visualizations of air flow by using vapor and smoke. We have concluded that fluid-like properties of air can be manipulated by additional external air flow sources, which has a potential to define ambient qualities in architectural space.

Experiment n.2 Underlying Air Flow with Smoke

134

EXAMINING AIR

Properties of Air Pressure We examined the power of dramatical change in air pressure with condensation nuclei as an output. It’s the same way clouds are formed.

Compressing air inside of the bottle with a bit of rubbing alcohol and releasing the pressure it forms a cloud through the reaction of dust and change of temperature.

Experiment n.3 Rapid Change in Total Pressure Condensation

Differences in Air Pressure In this experiment opposing sources of high and low temperature were placed within an air tight container. Two points of measurment were taken into consideration whilst trying to determine the difference in air pressure Millibars. A minor air flow was detected towards the cold end due to the minor difference in temperature.

Experiment n.4 Pressure Difference caused by Temperature

BACKGROUND RESEARCH

135

Vacuum Air Pressure In this experiment, we wanted to test if air pressure has the potential to be positive or negative. If the actual pressure is more than the atmospheric pressure, than its gauge pressure will be positive otherwise if it is less than atmospheric pressure its value will be negative . This negative gauge pressure can be called vacuum pressure, which we had a chance to test its load bearing capabilities. A unit volume of vacuum, in this case a glass, was able to carry twice its original volume filled with water.

Experiment n.5 Vacuum Load Testing

Artificial muscle constructed from properties of vacuum converted into surface tension.

136

EXAMINING AIR

Heated Air as Structural Method In this experiment we tested how much heat is required in order to expand a fixed amount of contained air. Heating the contained air inside would cause the air molecules to be driven further apart, therefore lowering its density. There was a visible difference in surface tension but lift was not reached because of weight.

Heated air principal diagram.

Experiment n.6 Reducing the density of air with heat.

BACKGROUND RESEARCH

137

Air as Catalyst Foam Emitter Dishwasher Liquid + Helium Pushing air onto a film of soap would end up in a bubble, if the air pushed towards the membrane is strong enough to rip through a part of that membrane through its boundaries and let surface tension of the soapy membrane find the most efficient form with using minimum amount of material in order to enclose maximum amount of volume.

If these soap bubbles are filtered through a dense cloth, much tinier soap bubbles will cluster collectively because of their cohesive properties, resulting in what we call foam.

In this sense, air flow has the potential to create tiny definitions of volume joined together efficiently. Due to foam being lightweight when the bubbles throughout the cluster are filled with helum the entire cluster can float.

This lift requires a balance between

the

amount

of

enclosed helium and cluster weight. A disturbance in this balance will cause the cluster to lose its floating capabilities.

138

EXAMINING AIR

Pressurized helium starts to be translated into foam. Forming the begginings of the cluster.

The

change

in

helium pressure and the decrease in the amount of soap within the

cloth

bigger

creates bubbles .

Reducing the overall density of the cluster.

External wind deforms the cluster as it grows.

The tip of the cluster bears nearly enough helium to float, so it is more resistant to external factors.

Once the cluster has more helium lift than its weight, it immediately rips of from the nozzle, floating approximatley 5 stories high.

BACKGROUND RESEARCH

139

Air as Catalyst Foam Emitter Patterning Strategies In the following experiment we tested how non-uniform filtration layer would result as deformed foam clusters.

Air

gaps

diameter

with

a

ranging

between

0.1mm

to 1.0mm were placed with multiple cores on top of the already-existing nozzle.

The increase in volume drastically reduces the cohesion strength in each bubbles resulting in significantly weaker and smaller clusters.

No bubbles were formed from the gaps which had a diameter more than 0.5 mm.

Foam filters relationship diagram.

140

EXAMINING AIR

BACKGROUND RESEARCH

141

Air Structures

Air

A single skin, enclosing a volume, which is stabilised by a small internal pressure differential.

Internal Pressure

Air-Supported

Air

Double skinned, the pressurised air contained within the volume enclosed by the two layers of membrane.

Atmospheric Pressure

Air-Inflated Air-Beam/ Air Cell / Buoyant

Solid Objects The vacuum created inside of the two layer fabric with light and fragile

Vacuum

elements inside provides great structural capacity for loading/bearing

Air-Deflated

150

EXAMINING AIR

Air-Supported Structures.

• Typically Fabric membrane enclosure anchored to the ground around the perimeter, and held aloft by low pressure pumped into the structure via air blowers or fan units • Supported by a small internal pressure difference • Uses airlocks (with two sets of doors) to prevent a sudden drop in pressure • Require constant or intermittent air to top up the relative internal pressure

Air-Inflated Structures Air-Beam • Air Beam support fabric structure • Using a series of parallel, conjoined air beams, large-spanning enclosures can be created • Low Pressure Air Beams (AZC’s Peace Pavilion operate at pressures as low as 17 kPa and often require a constant or intermittent air source to top up the pressure) • High Pressure Air Beams are more akin to a pneumatic tyre, where the beam is pressurized and has a valve for occasional maintenance Air-Cell • Air-inflated, double-skin, low pressure inflatables • Two membranes are connected with ties or diaphragm webs to form double-surface quilts rigidized with air • The fabric is held in place under tension from the internal, compressive force of air under pressure Buoyant • Can be achieved by reducing the density of air within a structure • Heating of air reduces its density and creates lift • Lighter-than-air gases such ass Hydrogen and Helium also can be used. But hydrogen is highly flammable and helium is finite resource.

Air-Deflated Structures • Experimental structures that use fragile elements inside of deflated double-sided fabric

BACKGROUND RESEARCH

151

Air Structures Air-Deflated Deflation Stiffness and Shape Manipulation Just like positive air flow in the previous iterations, the lack of air pressure therefore creating a vacuum also bears structural capabilities. In order to

achieve

a

deformation

with some rigidity, placing an array of plastic re-usable cups into a larger air-tight cushion perfomed

quite

effectively.

Geometrically, each single cup being defined as a cylinder with different different top and bottom radius values. When placed together a traingular gap is left between each row of cups. Forcing to remove this gap with vacuum eventually results in a taxonomy with load bearing capabilities.

Guidance of deflation through additional materiality.

156

EXAMINING AIR

Conclusions:

The

airtight

cushion could also be inflated.

The pattern of the cup layout determines the overall geometry to be either concave or convex.

Cups

don’t

have

to

be

rigid bodies. But some has to have some rigidity in order to

be load bearing.

BACKGROUND RESEARCH

157

158

FLIGHT CONTROL STUDIES

o Flight Control Studies *

Civil Aviation Control Systems

*

Aerial Vehicles

*

Natural Models

*

Dispersal Studies

*

Man-made Airdrop

*

AirDrop Control

o Examining Air *

Materiality of Air

*

Air as Catalyst

*

Air Architecture

*

Air Structures

o Material Adaptation *

Inflatable-Deflatable hybrid

*

Artificial Muscles

*

Stitching

*

Combining different rigidity levels

o Collective Intelligence *

Swarm Behaviour

*

Interaction Rule-sets studies

159

Inflatable-Deflatable Hybrid Patterning change Hybrid between Inflatable and Deflatables In this iteration we wanted to hybridize what we have learned, in terms of inflation/ deflation properties and surface materiality, into a single prototype. In order to achieve this a linear appendage was was embedded with inflateable cushions on one side and the opposing side was covered with anouther layer arrayed cups covered in an air-tight tubular continuum. This would result in opposing directionality of deformation caused by inflation and deflation.

Hybridizing inflation and deflation to determine directionality.

160

MATERIAL ADAPTATION

The prototype is inflated and deflated from a similarly positioned outlet. Difference in outlet layout and number of outlets could have a bigger impact

for

deformation.

The change in the cup layout resulted

deforming

the

prototype in an ‘s‘ shape rather than a single amplitude curve.

BACKGROUND RESEARCH

161

Artificial Muscles Artificial Muscle 1 Inflatable + Rigid Guiding Structure In this iteration we tested how an inflatable material’s volumetric

expansion

tendency

could be guided into a single direction, in order to achieve some sort of mobility or enclosure. A central point of inflation would transfer the pressurized air into 6 equally distributed extensions laid on a hexagonal layout. The compression resulting from the addition of a secondary non-elastic skin was able to let each arm of this prototype to perform as an artificial muscle.

Translation of surface compression caused by inflation into deformation.

162

MATERIAL ADAPTATION

Weight should be an important parameter of our design.

Inflating the extensions with helium (a gas less than air) significantly improved the velocity and the required air-pressure energy for the movement.

The partitions of air gaps in each extension should be more concise so that the transferred air in esgments of each individual arm has the potential to bear a more homogeneous distribution of surface pressure.

The overall design of the prototype should be more compatible with itself with the use of underlying geometric definitions. This would be important

whilst

dealing

with a large number of units.

BACKGROUND RESEARCH

163

Artificial Muscles Artificial Muscle 2 Patterning + Combined Materiality In this iteration our intention was to be able to guide both the directionality and the amount of exerted air pressure on the emission of ephemeral materials. In this case this was foam

The secretion of foam depended on several parameters includng; the surface area of the dishwasher liquid infused tip, the number of cushions and the surface area of connections inbetween each connected cushion to be able to regulate the air flow within a system which is not airtight.

made out of soap bubbles. An array of non-elastic air cushions with a dishwasher liquid infused tip was able to translate pressured air into foam, using the artificial muscle as a transformable nozzle.

An accordion-like expansion strategy with non-elastic sheeting plastic, compared with the first artificial muscle which depended on a secondary surface in order to perform, was much more efficient in terms of exerted air pressure and time of construction of a single prototype due to the standardization of segments.

Expansion of air flow through inflation.

164

MATERIAL ADAPTATION

The prototype is to be placed onto the desired location and orientation.

The

prototype

reaches

maximum amount of expansion.

The fluctuation in the frequency of exerted air pressure defines the position of the nozzle.

A constant flow of air would both keep the amount of volume throughout the prototype and the exerted ephemeral material.

BACKGROUND RESEARCH

165

Stitching Tests

Patterning

Patterning

Patterning

Patterning

166

MATERIAL ADAPTATION

The actuation of small space is controlled by the denser patterns of stitches on the upper surface of the pod. As the same amount of material inflates relatively less, it leads to opaque space, bringing inhabitants more privacy.

Patterning

Patterning

Patterning

Patterning

BACKGROUND RESEARCH

167

Stitching Tests

Patterning

Patterning

Patterning

Patterning

168

MATERIAL ADAPTATION

Catalogue

Iteration 00 We conducted series of simulations aiming to transform a 2d surface logic into a spatial one whilst making use of vertical drag caused by dropping. The first example builds the intuition of the effects of stiching having only one material.

BACKGROUND RESEARCH

169

Stitching Catalogue

Iteration 01 The first thing that we aimed to test was the relationship of parallel or perpendicular stiching alongisde the two types of anchoring possibilities.

Top View

Top View

Side View

Side View

From this iteration, it was quite clear that id the stiching curve was parallel along the anchor point that specific region between those those points became much more rigid.

170

MATERIAL ADAPTATION

Iteration 02 In this example; we tested the effect of having an internal offset as a guide for rigidity. The smallest triangular boundary is not to be crossed over.

Top View

Top View And having solely perpendecular stiches did not really affect the overall height, but it was a useful tool to actually subdivide the given boundaries into more diverse volumes.

Side View

Side View

BACKGROUND RESEARCH

171

Stitching Catalogue

Iteration 03 The edge conditions are also an important aspect of these tests.

In examples such as #12; if the adjacent vertexes allow curves to pass through them, a holistic deformation occurs with lower deformations rates.

Top View

Top View

Side View

Side View

This iteration ended up having the most overall mesh stress throughout this catalogue. But this was also the first time that we have observed a collective behaviour which was not solely dependant on the xyz axes, but was unsymmetrical.

172

MATERIAL ADAPTATION

Iteration 04 Here we could check that the directionality of the stiching patterns was directly linked with the height of the geometry.

Top View

Top View When the rigid and soft regions become very similar and repetitive, the pattern starts to have no overall effect throughout the surface.

Side View

Side View

BACKGROUND RESEARCH

173

Stitching Catalogue

Iteration 05 We tested how the density of parallel curves could be a second parameter guiding overall height.

Top View

Top View

Side View

Side View

Due to the nature of this specific pattern, the stiching becomes consideranly denser as we move further along the center of the given hexagonal boundary.

This was also guiding overall rigidity.

174

MATERIAL ADAPTATION

Iteration 06 In this iteration we tested how outlining certain predefined regions would translate into a volume.

Top View

Top View When the rigid and sof regions become very similar and repetitive, the pattern starts to have no overall effect throughout the surface.

Side View

Side View

BACKGROUND RESEARCH

175

Stitching Catalogue

Iteration 07 In this iteration we tested the effect of having circular and centric branches which would also underline the boundary of the given hexagonal shape.

When we get closer to the area centroid of the smallest triangle the number of neighbouring curves gets smaller.

Top View

Top View

Side View

Side View

Due to the nature of this specific pattern, the stiching becomes consideranly denser as we move further along the center of the given hexagonal boundary.

This was also guiding overall rigidity.

176

MATERIAL ADAPTATION

Iteration 08 In this iteration we tested the effects of leaving chunks of organized circular softer patches along the parts where we want to anchor this surface. It is very clear that we should be seeking for the opposite strategy.

Top View

Top View The seamless pattern was a very passive strategy to control the overall shape. Not having a higher hierarchy of pattern resulted in very unrealistic elasticity rates.

Side View

Side View

BACKGROUND RESEARCH

177

Stitching Catalogue

Iteration 09 In this iteration we tested the effect of having a combined space for a living pod on the intersections.

Top View

Top View

Side View

Side View

This outcome was both very flat and rigid. This was caused due to the pattern being very dense and also the edge condition tends to create a stronger connection.

178

MATERIAL ADAPTATION

Iteration 10 In this iteration we tested the effects of leaving chunks of organized circular softer patches along the parts where we want to anchor this surface. It is very clear that we should be seeking for the opposite strategy.

Top View

Top View The areas closer to the anchoring points should be the parts where we should seek most of the rigity if we are after overall structurla integrity.

Side View

Side View

BACKGROUND RESEARCH

179

Stitching Catalogue

Iteration 11 In this example; we aimed to test how allowing non-parrallel curvature towards the anchors from the central points of the hexagons would only be allowed if there was an apparent intersection of vertexes.

Top View

Top View

Side View

Side View

Due to the lower density of the stiching pattern, the overall formation was failing to response to any spatial quality, the geometry ended up being more or less flat.

180

MATERIAL ADAPTATION

Iteration 12 The edge conditions are also an important aspect of these tests.

In examples such as #12; if the adjacent vertexes allow curves to pass through them, a holistic deformation occurs with lower deformations rates.

Top View

Top View Achieving an overall twist created a higher rate of overall mesh stress in connection type 2. Already having this angular tension being forced to be pulled up more was too much for this iteration.

Side View

Side View

BACKGROUND RESEARCH

181

Stitching Catalogue

Iteration 13 In this example we aimed to test how certain intersections of stiching on the vertexes would actually turn out spatially.

Top View

Top View

Side View

Side View

The density of the inner spiral pattern was considerably higher.

This resulted in a configuration with much lower height.

182

MATERIAL ADAPTATION

Iteration 14 In this example we tested centric pathces of negative space in a stiching pattern which is running seamlessly along the gixen hexagonal boundary.

Top View

Top View The seamless pattern was a very passive strategy to control the overall shape. Not having a higher hierarchy of pattern resulted in very unrealistic elasticity rates.

Side View

Side View

BACKGROUND RESEARCH

183

Stitching Catalogue

Iteration 15 So, in order to make the overall geometry more controllable the idea of using patten as regions was introduced in this batch.

Top View

Top View

Side View

Side View

The overall configuration turned out to be too uncontrollable in terms of rigidity.

184

MATERIAL ADAPTATION

Iteration 16 In this example; we aimed to test how allowing non-parrallel curvature towards the anchors from the central points of the hexagons would only be allowed if there was an apparent intersection of vertexes.

Top View

Top View Due to the density of the stiching pattern, the overall formation was failing to response to any spatial quality, the geometry ended up being more or less flat.

Side View

Side View

BACKGROUND RESEARCH

185

Stitching Catalogue

Iteration 17 So, in order to make the overall geometry more controllable the idea of using patten as regions was introduced in this batch.

Top View

Top View

Side View

Side View

The overall rigidity pattern throughout the surface is way too homogeneous, not resulting in any volumetric actuations.

186

MATERIAL ADAPTATION

Iteration 18 Here the overall resolution of this patching is directly linked with the rigidity aspect of the composition.

Top View

Top View This resulted in very segregated patches in terms of rigidity. The contrast between soft and hard should be lower.

Side View

Side View

BACKGROUND RESEARCH

187

Stitching Catalogue evaluation Height Extent

Too High

Optimal

Too Little

192

MATERIAL ADAPTATION

After these tests we realised that the

stitched together brings more in

pure patterning is too passive and if

terms of having both structural

we work with airflow interaction the

stability and saving the form

interplay of rigid and elastic materials

resulted from air dropping.

Overall Mesh Stress

Too High

Optimal

Too Little

The overall mesh stress should

sort of structural integrity.

also be intelligently distributed throughout this formation, if we want the overall geometry to achieve any

BACKGROUND RESEARCH

193

Different Rigidity Levels Material Catalogue

Sheet Metal

TPU

ETFE

PVC

+

+

+

+

Strong

Flexible

Highly elastic

Light

Bending Properties

+

Resistant

Good Abrasion

Long life

Biocompatible

Resistant

Resistant

Stable

Recyclable

-

-

-

-

Long process

Short shelf life

Expensive

Not aesthetic

Expensive

Expensive

Sensitive

+

+

Tensile strength

to substances Very thick

194

MATERIAL ADAPTATION

+

Polyethelene

Latex

Silicone

The most elastic

+

+

+

Flexible

Highly elastic

Highly elastic

Resistant

+

Good Abrasion

Good Abrasion

-

-

-

Permanent

Shrinkage

Expensive

Environmentally friendly

+

+

Resistant Stable

Tensile strength

Deformation if Exposed to high, Or low temperature Bad compressibility

BACKGROUND RESEARCH

195

Different Rigidity Levels Physical tests Patterning Strategies The stored surface-tension of an inflated elastic material has the potential to both store and release pressurized air whilst, also being able to deform. Our first strategy to deform this property through patternation was one dimensional, where the intersection of the skin and the resistors is a line segment. A latex material with 0.2 mm thickness was used with non-elastic rope with 1mm diameter in cross section.

Pattern divisions

196

MATERIAL ADAPTATION

Layering Strategies Our second strategy to deform this property through patternation involved two dimensional intersections of patches and slits between the elastic skin and its resistors. A latex material of 0.05 mm thickness was combined

with

non-elastic

PVC with a thickness of 0.1 mm.

BACKGROUND RESEARCH

197

Different Rigidity Levels

t=1

Rigidity Pattern 01

t=2

Here we see three types materials with differing rigidities making use of vertical drag caused by dropping. As the central rigidity gradually increases, we can

t=3

determine how flat or how high a certain structure can result in.

t=4

198

MATERIAL ADAPTATION

Top View

Side View

Isometric The smaller pods closer to the anchors almost disappeared, not actuating at all.

The central column starts to form, regardless the patch being softer than the rest of the material.

BACKGROUND RESEARCH

199

Different Rigidity Levels

t=1

Rigidity Pattern 02

t=2

Here we see three types materials with differing rigidities making use of vertical drag caused by dropping. As the central rigidity gradually increases, we can

t=3

determine how flat or how high a certain structure can result in.

t=4

200

MATERIAL ADAPTATION

Top View

Side View

Isometric The overall surface is considerably flat. This is probably due to the most rigid part of the patches being directly located into the softest parts of the surface.

Once we introduce much more rigid elements into the composition, it is possible to see an overall distribution of much hihger stress.

BACKGROUND RESEARCH

201

Different Rigidity Levels

t=1

Rigidity Pattern 03

t=2

Here we see three types materials with differing rigidities making use of vertical drag caused by dropping. As the central rigidity gradually in-

t=3

creases, we can determine how flat or how high a certain structure can result in.

t=4

202

MATERIAL ADAPTATION

Top View

Side View

Isometric Making the central column much more rigid compared to the rest of the material did not give us the spatial quality of a central colum-like structure that we seek.

We see more actuation if the softer parts are liberated from the most rigid patches as a definitoin of singular volume.

BACKGROUND RESEARCH

203

Different Rigidity Levels

t=1

Rigidity Pattern 05

t=2

In these examples we tested how changing the amount of division applied on a surface would also differ in its performance. More divisions we get - more likely

t=3

that we can keep the tension collected from dropping.

t=4

204

MATERIAL ADAPTATION

Top View

Side View

Isometric The overall stress throughout the units is quite low. More resolution in pattern is required if we seek structural integrity.

The difference in terms of vertical actuation of the circular softer patches are differnt quite radically within themselves.

BACKGROUND RESEARCH

205

Different Rigidity Levels

t=1

Rigidity Pattern 06

t=2

In these examples we tested how changing the amount of division applied on a surface would also differ in its performance. More divisions we get -

t=3

more likely that we can keep the tension collected from dropping.

t=4

206

MATERIAL ADAPTATION

Top View

Side View

Isometric Decreasing the number of rigid linear elements within the unit allowed for more deformation.

Increasing the resolution which we introduce softer patches of habitable spaces made the whole deformation more homogeneous pared

to

comRigidity

Pattern 05.

BACKGROUND RESEARCH

207

Different Rigidity Levels

t=1

Rigidity Pattern 07

t=2

In these examples we tested how changing the amount of division applied on a surface would also differ in its performance. More divisions we get

t=3

- more likely that we can keep the tension collected from dropping.

t=4

208

MATERIAL ADAPTATION

Top View

Side View

Isometric The softer pathces

having

their

smaller subdivisons pre-connected both were able to increase overall resolution of pod placement and amount of are affected by drag.

Compared to Pattern 06, this iteration bears more structural integrity. Because the pinches around the softest patches allow to build up on pre-connected tension.

BACKGROUND RESEARCH

209

Different Rigidity Levels

t=1

Rigidity Pattern 08

t=2

We also wanted to test the effect of porosity, meaning how much can we actually increase the surface area effected by the same drag received from drop-

t=3

ping. And to also threshold of elasticity to actually be able to resist that force.

t=4

210

MATERIAL ADAPTATION

Top View

Side View

Isometric The overall surface is considerably flat. This is probably due to the most rigid part of the patches being directly located into the softest parts of the surface.

To be stable and safe while the free fall, it needs a strong and efficient control to react to the environmental values in real-time.

BACKGROUND RESEARCH

211

Different Rigidity Levels

t=1

Rigidity Pattern 08

t=2

We also wanted to test the effect of porosity, meaning how much can we actually increase the surface area effected by the same drag received from drop-

t=3

ping. And to also threshold of elasticity to actually be able to resist that force.

t=4

212

MATERIAL ADAPTATION

Top View

Side View

Isometric The placed living pods (softest patches) are ineffective in terms of both additional drag and overall surface deformation and any volume to be created.

As we get closer to the edges due to the lack of perpendicular patterning we lose control.

BACKGROUND RESEARCH

213

Different Rigidity Levels

t=1

Rigidity Pattern 09

t=2

We also wanted to test the effect of porosity, meaning how much can we actually increase the surface area effected by the same drag received from drop-

t=3

ping. And to also threshold of elasticity to actually be able to resist that force.

t=4

214

MATERIAL ADAPTATION

Top View

Side View

Isometric Compared to rigidity Pattern 08, the increase in the resolution of pod placement formed a much more responsive output in terms of both overall mesh stress and volume.

Because the softer patches gets smaller as we move towards the anchor points, the first thing that the bigger and more elastic patches are reacting to are the central rigid elements forming the column. This sudden transition could be improved.

BACKGROUND RESEARCH

215

Different Rigidity Levels Interaction between parts - outer layer and living pods Patterning the outer layer with different materials consisted

weight of living pods and collision with the ground, similar

of dfferent rigidity levels can help to control the outer layer.

to landing. The way the outer layer transforms depends on

However, we have cellular spaces or living pods as well

many factors: the placement of the pods, the amount of

that are connected with it. Their interaction is what we

their inflation, which is interrelated with the amount of drag

decided to test. First simulating the inflation up, similar to

in the beginning and then their weight in the end, rigidity

contracting airflow during dropping, and then introducing

patterning and the connections between units.

Rigidity Pattern

Max

216

Elasticity

Min

MATERIAL ADAPTATION

Actuated living pod

The most elastic region

More rigid region

The most rigid region

BACKGROUND RESEARCH

217

Different Rigidity Levels

Actuated living pod

218

MATERIAL ADAPTATION

The most elastic region

More rigid region

The most rigid region

Actuated living pod

The most elastic region

More rigid region

The most rigid region

BACKGROUND RESEARCH

219

o Flight Control Studies *

Civil Aviation Control Systems

*

Aerial Vehicles

*

Natural Models

*

Dispersal Studies

*

Man-made Airdrop

*

AirDrop Control

o Examining Air *

Materiality of Air

*

Air as Catalyst

*

Air Architecture

*

Air Structures

o Material Adaptation *

Inflatable-Deflatable hybrid

*

Artificial Muscles

*

Stitching

*

Combining different rigidity levels

o Collective Intelligence *

Swarm Behaviour

*

Interaction Rule-sets studies

Swarm Behaviour Swarms & Emergency Response Swarms are appealing as decentralized robotic systems for emergency response because compared to centralized systems designed for the same task, they had very simple components (relatively cheap) and maybe interchangeable (which make the system robust).

Another big advantage is reliability. Since the swarm is in general highly redundant, the swarm can be designed to survive through many kinds of disturbances, which is possibly more severe than those considered in standard control systems. Because of redundancy, the swarm would have the ability to adapt dynamically to the working environment.

Swarm Intelligence Swarm intelligence(SI) is the collective behavior of decentralized systems. SI systems consist typically of a population of simple agents or “Boids” (Reynolds, 1987) interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents.

Image 1. Basic local rule-sets in Boids (Reynolds, 1987)

Alignment

Cohesion

Avoidance

Parameters Test images for various configuration

Those properties above show the basic parameters in swarm behaviour.

with different local rule-set values

Alignment: steer towards the average heading of local mates.

— Image 1-6 on the right page

Cohesion: steer to move toward the average position (centre of mass) of local mates.

Other configuration images with ob-

Avoidance: steer to avoid crowding local mates.

stacles and attractors — Image 7-8 on the right page

The existence of “Obstacle” and “Attractor” are also additional parameters related to the properties above by changing the configuration of units.

222

COLLECTIVE INTELLIGENCE

Cohesion: 8; Alignment: 5; Avoidance: 2

Cohesion: 5; Alignment: 3; Avoidance: 10

Cohesion: 25; Alignment: 25; Avoidance: 1

Cohesion: 1; Alignment: 1; Avoidance: 25

Cohesion: 25; Alignment: 1; Avoidance: 1

Cohesion: 1; Alignment: 25; Avoidance: 1

Atrractor

Obstacle

BACKGROUND RESEARCH

223

Interaction Rule-sets Studies

Behaviour Interaction Rule-set

Material Output Catalogs

224

COLLECTIVE INTELLIGENCE

Depending on the environmental values, a certain interaction rule-set would be acted as a decision making process.

The specific behaviour from the rule-set brings out a specific type of material output as trails that can become air-based material.

Interaction Rule-set

Material Output

BACKGROUND RESEARCH

225

Interaction Rule-sets Studies Rule-set 1

Avoidance

Air-based Material Flying Units

The top view showing the interaction rule-set and the air flow changes based on time.

— images on the left page

The consequent material out put and the phase changes.

— images on the right page

226

COLLECTIVE INTELLIGENCE

The first interaction rule-set introduces the behaviour of “Avoidance”. Both air-based material and flying units avoid from each other, leading to a continuous loop of avoidance.

For example, the material group get dispersed from the flying units in the beginning. Then, if the materials go closer to flying units, they will be also scattered.

Because of the behaviour of “Avoidance”, the bifurcated pattern of dispersion brings about the following figures of the material output. The state of the overall form is transient based on time and keeps changing itself by the behaviour of units.

BACKGROUND RESEARCH

227

Interaction Rule-sets Studies Rule-set 2 Bigger

Smaller

Avoidance + Alignment

Air-based Material Flying Units Influencer

+

The top view showing the interaction rule-set and the air flow changes based on time.

— images on the left page

The consequent material out put and the phase changes.

— images on the right page

228

COLLECTIVE INTELLIGENCE

In the second rule-set model, the Influencer leads to “Avoidance” and a different type of “Alignment” between groups.

If the material group goes closer to the influencer, it will become bigger and easier to be detected by the flying units.

Therefore, its size will get smaller again and the transformation in scale is a continuous loop, while controlled by the interaction between the Influencer and the flying units.

Consequently, the higher density of the material output appears when it becomes larger(fig. 01-06 on the right page) and the lower density of the structure is materialized when it gets smaller. (fig. 07-10 on the right page) By this mechanism, it can respond to rapid changes of needs by offering different density of shelters.

BACKGROUND RESEARCH

229

Interaction Rule-sets Studies Rule-set 3

Alignment + Cohesion

Air-based Material Flying Units

The top view showing the interaction rule-set and the air flow changes based on time.

— images on the left page

The consequent material out put and the phase changes.

— images on the right page

230

COLLECTIVE INTELLIGENCE

The third rule-set model features different types of “Alignment” and “Cohesion” with several numbers of micro-groups. While following the leaders per each group which are in a blue color, it shows a certain patternized density in each directionality.

Through each localized density based on environmental values and time, we could achieve the different phases of the density and the directionality in the material output.

BACKGROUND RESEARCH

231

Interaction Rule-sets Studies Rule-set 4

Alignment + Cohesion + Avoidance

Air-based Material Flying Units Obstacle

The top view showing the interaction rule-set and the air flow changes based on time.

— images on the left page

The consequent material out put and the phase changes.

— images on the right page

232

COLLECTIVE INTELLIGENCE

The fourth is an interaction model equal with the third rule-set added with “Avoidance”.

Because of the presence of the obstacle, the flocking units get spread out the most in this model.

Hence, the largest spatial enclosure catalog is created through the behaviour of spreading out with the variuosly patternized density of groups.

BACKGROUND RESEARCH

233

234

05

The Unit o Prototype 01 o Prototype 02 o Prototype 03 o Prototype 04/05 o Prototypes Evaluation

THE UNIT

235

Prototype 01

236

Top View

Front View

Aggregation Joints

Robotic Net controlling free fall

Spatial Actuation

BACKGROUND RESEARCH THE UNIT

237

Prototype 01 Timeline

Delivery

Unpacking Units unfold into a hexagonal copter topology from maple seed-like formation.

Manuevering Each blade has the ability to change its angle allowing the unit to chsnge directionality according to environmental inputs.

Aggregation After locking into their horizontal formation, units undergo into a transformation allowing it to have parachuting properties.

Landing

Land Operation 238 238

PROTOTYPE 01

Prototype 01 Delivery

2.74 m 3.02 m

Units in packed state

Our deployable units are tightly packed inside of an airdrop aircraft. 500 units fit in a small standard model used for military airdrop.

12.19 m

C-130 Hercules - standard airdrop aircraft

Unpacking The first phase of controlled air dropping starts when the airplane reaches the location that was chosen for the shelter settlement. Units unpack by chemically inflating its Skeleton, in order to take a designed shape optimized for manoeuvering purposes.

THE UNIT

239

Prototype 01 High-altitude: manoeuvring Controlling Free Fall In order to control free-fall in a vertical manner we used properties that we’ve learned from natural dropping models and applied them to the unit’s design. Before unlocking, a single arm ( just like the maple seed) has the capability to create lift solely on its geometry. After unlocking the unit achieves a hexagonal copter formation. By changing the angle of attack on each wing seperatley the unit has a chance to get different sets of motion. The robotic net, which spans the entire units guides both this angle and the amount of porosity to control and make decisions for overall velocity.

Contraction

Expansion

Inflated Sheet Aluminum

Wind tunnel simulation of rotational airflow.

240

PROTOTYPE 01

THE UNIT

241

Prototype 01 Low-altitude: aggregation After units are maneuvered into the approximate location, units make decision how to aggregate to spatialize into actual shelters responding to different parameters of the land in the low altitude. For example, not only flight requirements for stability of the body, the environmental condition and urban models of the target area also act as important parameters to take different aggregation strategies.

To achieve that, our control system leads units to take a decision of an aggregation strategy depending on the input parameters and, consequently, we could achieve generative body plans as outputs adaptative to the needs of the existing condition of the target area.

Overall System

Input Parameters Environment Urban Model Decision Making Flight Requirements

Aggregation Strategy

Output: Body Plans

242

PROTOTYPE 01

THE UNIT

243

Prototype 01 Landing After the aggregation phase, when more complex typologies were built, the main challenge of the landing phase is to secure a safe landing and to start to spatialize the unit. This transition moment between a passively flying vehicle and almost inhabitable space is conducted by actuating the parachuting layer between skeletons.

If you’re attermpting a power-off

S-turns while on final approach Another way of modifying the

glide, you can purposely modify

also can bring another effective distance you travel is to purposely

your pattern to make the runway.

way to modify landing pattern, overshoot your turn to final. especially if your’re following slower traffic ahead.

Pattern adjustment scenarios

Path planning and trajectory tracking algorithms considering wind compensation

244

PROTOTYPE 01

THE UNIT

245

Prototype 01 Land Operation After the deployment part we understand that settlement’s operation is also a process, that has its own needs. We are planning to address them through the same features of the unit, aiming for architecture that is sustainable, self-sufficient and adaptable.

The units land already self-built in specific groups of clusters that correspond to the urban model of the settlement and harvesting water strategy. Using such feature as unpacking our units can dramatically Horizontal elements(above) - McKibben Air Muscle and Vertical elements(below) of robotic nets - APAM, UC Santa Barbara engineering

change their architectural typology, responding to programmatic changes or situations that lead to change in numbers.

The robotic net, not only enables the angle of attack manoeuvring, but also adapts after to population needs, creating the partitioning and heterogeneity of space. The unit’s ability to change we embodied in robotic net. While in deployment phase it enables manoeuvring in the air, its architectural task is to adapt to inhabitants, maintaining structural integrity. Consisted of different types of air muscles, net has an ability to dramatically change the shelter’s form and partitioning, physically dividing the space by embedded columns in joints and walls between them.

1. Atmospheric water harvesting net

Changing spatial relationship by robotic nets.

2. Transparent skin - parachute 3. Robotic net 4. Inhabitable enclosures

Unit

Warka Towers, Arthuro Vittori

246

PROTOTYPE 01

Section diagram

Unit scale

THE UNIT

247

Prototype 02

248

Top View

Front View

Pre-aggregation

Controlling Free Fall : by weight / centre of mass regulation

Spatial Actuation

Timeline

THE UNIT

249

Prototype 02 Timeline

Tightly packed inside of an airdrop aircraft. 500 units fit in a small standard model used for military airdrop.

The first phase of controlled air dropping starts when the airplane reaches the location that was chosen for the shelter settlement.

Units unpack by chemically inflating its “Skeleton”, in order to take a designed shape optimized for manuevering purposes.

The difference in weight throughout this constructed landscape causes certain parts in the aggregation, turning into a parachute state.

Controlled air dropping is not only the fastest and most autonomous way to deploy, but also uses free fall as a self-actuation or selfstructuring emergency shelter settlements.

When the aggregation reaches the ground, living pods autoinflate using controlled chemical reactions which create internal air pressure.

250

PROTOTYPE 02

Prototype 02 Flight Regulation Mechanisms Energy Generation Gravitational potential energy

The potential energy achieved

Kinetic

cargo plane is to be converted

Electric

and stored into electric energy

by merely being inside a flying

through strategically placed turbines and directing airflow.

Detachment Mechanism

Actuator connected to microcontroller

When aggregation need to separate mechanism releases part of the cloth (plastic sheet) of connected unit

THE UNIT

251

252

PROTOTYPE 02

Prototype 02 Flight Regulation Mechanisms Localization Wind speed and direction sensor (in one unit per aggregation)

GPS module

Altitude sensor (atmospheric pressure barometric pressure)

WiFi Connection module

Parachuting Free-fall wind tunnel tests, depending on vertex strength.

Contracted airflow while falling

Chemical Inflation Mechanisms By chemical inflation mechanisms, the weight and centre of mass are regulated to control free fall.

Weight

Water storage

Water to dissolve chemical capsules and start chemical reaction

Connected to water collectors

Dissolvable packet Chemical Capsules

Possibility to add more capsules after landing

Chemical inflation of Air Pockets (CO2) Citric acid crystals

Baking powder

THE UNIT

253

Prototype 3a

254

Alternative A. For the prototype 3, we tried to combine more mechanical control with parachute logic.

The core consisted of inflated sheet metal was controlling the opening of the unit or unfolding and then manoeuvring.

Manoeuvring

This option is possible to manoeuvre in both local and collective scales by controlling the angle of attack by controlling the shape of the parachute, similar way as parafoils.

Spatial Actuation

After landing, the way of unfolding the skeletons lead to the form of the unit.

THE UNIT

255

Prototype 3b

256

Alternative B. However, both the option A and B had two constraints. Firstly, they are still lack of the diversity of spatilization. And because of that, secondly, it is not capable to elaborately manoeuvre by dramatically controlling the centre of mass in real-time.

Manoeuvring

The

option

B

controls

manoeuvring

by

opening

and closing and controlling directionality in only aggregation level changing the centre of mass collectively.

Spatial Actuation

In a local scale, it creates different degrees of scale and height of the structure.

THE UNIT

257

Prototype 4/5 Multi Layered System

258

Details of Multi Layered Structure

Section

Top view

Top View

Side view

Isometric view

Manoeuvring & Spatial Acutation

Interrelationship between the outer layer & living pod actuation

THE UNIT

259

Prototype 04/05 Multi Layers Multi Layers Structure After all these iterations, we

Stitching Patterns

Upper Layer

came to our latest unit design.

To have more drag-surface ratio, we came up with multiple layers surface. The upper

Living Pods

layer on which the overall stitch patterning strategies are applied is becoming a shell

Bottom Layer: floor

that has a certain structural capacity,providing a “Shed”.

1 Unit Each unit can take a decision how many living pods to actuate, one in minimum and six in maximum, influencing the overall morphology. Living pods

Upper Layer

have controlled holes changing their height and scale by air channeling, and provide cellular spaces.

Unit-to-unit Connection

Living Pods

Upper Layer is connected to bottom layer through living pods and columns with anchors. To keep a certain height between other units, they can take a decision to actuate the column or not.

Column & Anchor

260

PROTOTYPE 04/05

Upper Layer

Living Pods Bottom Layer: floor

deflated

actuated

Inflated Columns

Deflated Column

Activated Anchor Ground

THE UNIT

261

Physical Experiments Air Channeling Betwen Multiple Surfaces

Iteration 01

We examined this multi-layered logic designing a physical experiment.

Two pieces of fabric were connected in 3 different ways and we tested what kind of formations results from

Upper layer

contracting a strong airflow, like the one while dropping. The most connected option proved to be the least interesting in terms of control. Others showed that there is a possibility to control

Bottom layer

each layer separately.

Side View

262

PROTOTYPE 04/05

CONNECTIONS BETWEEN LAYERS

HOLE

WEIGHT

Iteration 02

Iteration 03

Side View

Side View

KNOT

THE UNIT

263

Physical Experiments

Freefall Behaviour with Air Pockets

Iteration 01

Inflated area

-Orientation: unstable, could not

Area where air didn’t stay

harness drag.

-Overall form: the interior volume barely inflated.

Unit’s displacement on the XY plane was considerably little.

Iteration 02 -Orientation: unstable, could not harness drag. Fell slower compared to iteraton 01.

-Overall form: the interior volume barely inflated.

Unit’s displacement on the XY plane was higher.

264

PROTOTYPE 04/05

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

THE UNIT

265

Physical Experiments Proof of Concept: Prototype 04/05

#1 Dropping

#3 Stablization 266

#2 Unfolding

#4 Landing

267

Technical Details Orientation

GPS

Altitude Sensor

Interaction (a)

(b)

Radio(a) + Bluetooth module(b)

Environment Sensing

Wind Sensor : measure strength&direction, one per a super pack

Energy Generation

Electric energy generator from wind : through magnetic induction

268

PROTOTYPE 04/05

Chemical Inflation (a) (b)

Packed Unit

(c) Chemical capsules(a) + Water(b) + Pipe(c)

Wind Inflation

Opening/closing holes

Attaching to other units

(a) (b) QR code(a) + Electromagnets(b) : QR code with information about each column/unit

Kinect-like sensor : camera + laser

Landing

Kinect-like sensor

Anchor THE UNIT

269

Prototypes Evaluation Manoeuvring & Spatial Actuation

Manoeuvring Interdependency

SPATIAL ACTUATION by dropping

Char t 1. Each protot ype is evaluated based on the two criteria: manoeuvring capacity and interdependency in the flight.

270

PROTOTYPE 04/05

THE UNIT

271

272

06

PreDeployment o Hybrid System o Precalculation of the Deployment o Context Specific Settlements o Flight Calculation

PRE-DEPLOYMENT

273

Hybrid System RADICAL GRAVITY CONTROL SYSTEM = strong & efficient control

CENTRALIZED

non-resilience

To be stable and safe while the

However, most machines fail in

free fall, it needs a strong and ef-

case of unexpected damage be-

ficient control to react to the en-

cause of a low level of adaptive-

vironmental values in real-time.

ness. Hence, our system requires a robust performance under uncertainty.

DECENTRALIZED

274

HYBRID SYSTEM

adaptive relationship

uncontrollable

To negotiate with high popula-

On the other hand, we aim to

tion of parameters of free fall in

avoid an uncontrollable situation

real-time, it needs adaptive rela-

with too much liberated agents

tionship between individual units.

while free fall.

[+]

[+]

[+ ]

PARAMETERS

[

BEHAVIOUR

FORMATION

: Input

]

: Rule-sets

: Output

PRE-DEPLOYMENT

275

Precalculation of the deployment Main parameters Prior to the deployment it’s important to have an

dropping point and the settlement model based on the

approximate idea of the suitable settlement and

parameters such as: climate, terrain, type of disater. Our

alleviate highly choreographed process of dropping.

approach is global, but we believe our settlements should

For this we calculate the overall flight with the

respond to the context and the environment.

1. Urban Region

Parameter Influences

2. Topography 3. Climate 4. Wind

As a result the system generates

Formation typologies of network

the settlement model, translating it to the units language, overall navigation strategy and the dropping point.

276

PRECALCULATION OF THE DEPLOYMENT

Application of parameters

Agents behaviour driven by parameters

Behaviour formation out of parameteres and behaviour rule-sets PRE-DEPLOYMENT

277

Paramter 01. Urban Region — output 01

Paramter 02. Topography — output 01

Paramter 02. Topography — output 02

Paramter 02. Topography — output 03

278

PRECALCULATION OF THE DEPLOYMENT

Paramter 01. Urban Region — output 03

Paramter 03. Wind — output 01

Paramter 03. Wind — output 02

Paramter 03. Wind — output 03 PRE-DEPLOYMENT

279

Context Specific Settlements Urban Region Parameter

Outputs

Aerial black and white images of urban areas. City blocks are indicated in black whilst; roads,streets, open urban and public areas are indicated in white.

— consecutive iterations on the spread, + (t = 3 seconds)

280

CONTEXT SPECIFIC SETTLEMENTS

Output 01 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold was increased due to current environment posessing strong curves, such as the river and avennues.

Path Tracking: Number of paths mostly tend to decrease, forming major channel lines.

Boundary Condition: The boundary condition is bouncing back, which means we want concentrate the agents more or less within the same area in a dense way.

PRE-DEPLOYMENT

281

Context Specific Settlements Urban Region Parameter

Outputs

Aerial black and white images of urban areas. City blocks are indicated in black whilst; roads,streets, open urban and public areas are indicated in white.

— consecutive iterations on the spread, + (t = 3 seconds)

282

CONTEXT SPECIFIC SETTLEMENTS

Output 02 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold became less than usual beacuse the units were able to abstract the shape of the existing body of water into a flow logic.

Path Tracking: Number of paths mostly tend to decrease, forming major channel lines.

Boundary Condition: The boundary condition is kill, which means we were not really interested in the traces of the units which crossed the designated border. The aggregation’s interaction with the water body was more interesting for us.

PRE-DEPLOYMENT

283

Context Specific Settlements Urban Region Parameter

Outputs

Aerial black and white images of urban areas. City blocks are indicated in black whilst; roads,streets, open urban and public areas are indicated in white.

— consecutive iterations on the spread, + (t = 3 seconds)

284

CONTEXT SPECIFIC SETTLEMENTS

Output 03 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold was decreased so that units make more use of the area provided between two urban formations.

Path Tracking: Number of paths mostly tend to decrease within the urban area while increasing in more rural areas where we need more exploration.

Boundary Condition: The boundary condition is bouncing back, which means we want concentrate the agents more or less within the same area in a dense way.

PRE-DEPLOYMENT

285

Context Specific Settlements Topography Parameter

Outputs

Topography curves used to abstract existing geophysical conditions. A black base is used due minimize extreme slope conditions.

— consecutive iterations on the spread, + (t = 3 seconds)

286

CONTEXT SPECIFIC SETTLEMENTS

Output 01 agent network output: trails of behaviour dispersion concentration

Number of Units: 500 Wandering Parameter: Steering threshold was increased so that the units can align more precisley to the amount of information gathered from a bigger resolution and scale of topography in this scenario.

Path Tracking: Number of paths mostly tend to increase as the agents move along to the area where there is less slope.Meaning less contour lines.

Boundary Condition: The boundary condition is bouncing back, which means we want concentrate the agents more or less within the same area in a dense way.

PRE-DEPLOYMENT

287

Context Specific Settlements Topography Parameter

Outputs

Topography curves used to abstract existing geophysical conditions. A black base is used due minimize extreme slope conditions.

— consecutive iterations on the spread, + (t = 3 seconds)

288

CONTEXT SPECIFIC SETTLEMENTS

Output 02 agent network output: trails of behaviour dispersion concentration

Number of Units: 500 Wandering Parameter: Steering threshold was decreased so that the units this time are much direct in trajectory than before. Reaching to the other side of this setup much faster within this topography condition.

Path Tracking: Number of paths mostly tend to increase as the agents move along to the area where there is less slope. Meaning less contour lines.

Boundary Condition: The boundary condition is bouncing back, which means we want concentrate the agents more or less within the same area in a dense way.

PRE-DEPLOYMENT

289

Context Specific Settlements Topography Parameter

Outputs

Topography curves used to abstract existing geophysical conditions. A black base is used due minimize extreme slope conditions.

— consecutive iterations on the spread, + (t = 3 seconds)

290

CONTEXT SPECIFIC SETTLEMENTS

Output 03 agent network output: trails of behaviour dispersion concentration

Number of Units: 500 Wandering Parameter: Steering threshold was increased so that the units can align themselves accordingly to a topography condition which has less slope and resolution of information compared to the previous scenarios. The agents tend to explore more on less slope.

Path Tracking: Other than the beggining part it is very unlikely that the agents will compute a major artery to direct collective movement.

Boundary Condition: The boundary condition is bouncing back, which means we want concentrate the agents more or less within the same area in a dense way.

PRE-DEPLOYMENT

291

Context Specific Settlements Climate 01. Cold Climate Suitable for cold climate and linear terrain conditions where less slope is desired.

Terrain-Cold Climate

time

Terrain-Hot Climate

time

02. Hot Climate Introducing public spaces within the aggregation allows more possibilities

for

guiding

the

directionality.

292

CONTEXT SPECIFIC SETTLEMENTS

03. Hot Climate Suitable for hot climates due to the negative space left out acting as a courtyard and regulating ventilation.

Urban-Cold Climate

time

04. Cold Climate The densest one out of all iterations. suitable for narrow edges and corners which could be a highway, or vast avenues Hot-Hot Climate

time

within an urban context.

PRE-DEPLOYMENT

293

Context Specific Settlements Parameter Terrain: 3% hill Climate: Hot, dry Affected People: 15,000

294

CONTEXT SPECIFIC SETTLEMENTS

Parameter Terrain: 8.68% hill 1

2

Climate: Cold Affected People: 13,000

PRE-DEPLOYMENT

295

Context Specific Settlements Parameter Terrain: Flat Climate: Tropical Affected People: 18,000

296

CONTEXT SPECIFIC SETTLEMENTS

Parameter Terrain: 19% hill 1

2

Climate: Tropical Affected People: 9,000

PRE-DEPLOYMENT

297

Flight Calculation Path Optimization according to Wind Data Parameter

Outputs

Images of visualized wind velocity data. The landmasses are indicated in black so that they would act as a target within this non central starting condition.

— consecutive iterations on the spread, + (t = 3 seconds)

298

FLIGHT CALCULATION

Output 01 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold was decreased so that the units resist more to the existing wind conditions.

Path Tracking: On this scale we want our agents to be more less more guided than the previous tests.

Because the start condition is nonlinear, path tracking behaviour also does path optimization between different black landmasses.

Boundary Condition: The boundary condition is nonexistent. The units are able attract themselves enough not to just wander off the boundary.

PRE-DEPLOYMENT

299

Flight Calculation Path Optimization according to Wind Data Parameter

Outputs

Images of visualized wind velocity data. The landmasses are indicated in black so that they would act as a target within this non central starting condition.

— consecutive iterations on the spread, + (t = 3 seconds)

300

FLIGHT CALCULATION

Output 02 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold was increased. So the units form the most efficient connection between different landmasses regarding exerted wind pressure.

Path Tracking: On this scale we want our agents to be more less more guided than the previous tests.

Because the start condition is nonlinear, path tracking behaviour also does path optimization between different black landmasses.

Boundary Condition: The boundary condition is nonexistent. The units are able attract themselves enough not to just wander off the boundary.

PRE-DEPLOYMENT

301

Flight Calculation Path Optimization according to Wind Data Parameter

Outputs

Images of visualized wind velocity data. The landmasses are indicated in black so that they would act as a target within this non central starting condition.

— consecutive iterations on the spread, + (t = 3 seconds)

302

FLIGHT CALCULATION

Output 03 agent network output: trails of behaviour dispersion concentration

Number of Units: 300 Wandering Parameter: Steering threshold was decreased so that the units resist more to the existing wind conditions.

Path Tracking: This is the first iteration where we have reached a loop condition which lasts more than t=12 within the simulation context. Both wind and landmass alter tracking conditions to behave this way.

Boundary Condition: The boundary condition is nonexistent. The units are able attract themselves enough not to just wander off the boundary.

PRE-DEPLOYMENT

303

Flight Calculation Wind Zones

Wind Velocity

35,000 feet : conditions at the Jet Stream

11,500 feet : conditions at the high planetary boundary

4,900 feet : conditions at the low planetary boundary

300 feet : near sea level conditions

Data based on the open source

0 feet

: Camarillo Weather (www. camarilloweather.com)

: conditions at the surface

304

FLIGHT CALCULATION

Data based on the open source : Camarillo Weather (www. camarilloweather.com) Example Area: 3.00° N, 35.94° E

(1) 25,000 feet

Wind Velocity (1)

(2) 15,000 feet

Wind Velocity (2)

Wind Velocity (3) (3) 5,000 feet

(4) 500 feet Wind Velocity (4)

PRE-DEPLOYMENT

305

Flight Calculation Path Optimization according to Wind Data Wind Compensation Effected movment by wind

Optimised agent’s behaviour

TARGET AREA

HELPER

OBSTACLE

IN-BETWEEN

High Population

Control System By sensing the wind direction and transferring this information to others, units search for the optimized path to the destination and choose the most suitable position to aggregate with other unit after calculating the cost field map.

Rule-set out of Cost Field Furthermore, units can set a rule-set of both local and collective scales. For example, they can choose where to unfold to move in a certain direction and the choreography strategy in a collective scale.

306

FLIGHT CALCULATION

Wind Types

Behaviours

TARGET AREA

Airflow Map As one of the main parameter for air dropping is wind, we divided it to three categories, which lead to different decision making of units. The real-time airflow map affects on the actual movement different from the desired one.

Conversly, using that, we can optimise unit behaviours by

calculating

the

wind

compensation logic.

Cost Field Map By introducing the cost field map, the units try to follow the helper wind, avoid the obstacle wind and, with the third type, calculate their movement adding wind compensation.

Calculation of the dropping point

DEPLOYMENT POINT

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TARGET AREA

PRE-DEPLOYMENT

307

308

07

Deployment o AirDrop o Forming Bodyplans *

Choreography Tests

*

Connections Tests

*

Forming Bodyplans

o Unfolding Parachute

DEPLOYMENT

309

Overall Timeline

310

TIMELINE

DEPLOYMENT

311

AirDrop Packing and release The units are tightly packed in an airdrop airplane. We calculated how many units we can fit in the big cargo airplane and designed ‘superpacks’. In a radius of 3 kilometers of target area the airplane releases them around 23 000 feet.

Unit in a packed state

19000

45000.00

Superpack

1900

5500.00

The section of the airplane with dimenstions showing our superpacks inside.

314

AIRDROP

23 000 feet Dropped from the airplane Airdropped around 23000 feet

15 000 feet Forming bodyplans

Superpack

12 000 feet Unfolding the parachute

Manoeuvring

4 000 feet Aggregating

2 000 feet Landing

Land

DEPLOYMENT

315

Choreography tests From local rule-sets based on rotation and small displacement Phase A

Phase B

Phase C

Phase D

Phase E

318

FORMING BODYPLANS

Rule-set: Hierarchy 1 Pairs: 2 Rotation: 30 degrees Hierarchy 2 Pairs: 4 Rotation: 60 degrees Hierarchy 3 Pairs: 2 Rotation: 180 degrees Hierarchy 4 Pairs: 4 Rotation: 90 degrees

While units are still tightly packed and quite mobile, we started to think how to form more context and flight specific aggregations with minimal efforts. We started to simulate how through simple local rule-sets based on rotation and small displacement we can achieve different choreographies.

Each choreography changes the angle of each local arrangement.

When the wind is a helper, it increases surface area of the whole aggregation horizontally to speed up(Phase B and C), and takes minimal aggregation to decrease surface area in a strong wind condition.

Perspective View DEPLOYMENT

319

Choreography tests From local rule-sets based on rotation and small displacement Phase A

Phase B

Phase C

Phase D

Phase E

320

FORMING BODYPLANS

Rule-set: Hierarchy 1 Pairs: 3 Translation: x+1 Hierarchy 2 Pairs: 3 Rotation: 120 degrees Hierarchy 3 Pairs: 12 Translation: (x+y)+1 Hierarchy 4 Pairs: 5 Rotation: 72 degrees

This type of choreography performs more centralised control in a bigger scale. Each local group is also organised to be able to negotiate its orientation.

Perspective View DEPLOYMENT

321

Choreography tests From local rule-sets based on rotation and small displacement Phase A

Phase B

Phase C

Phase D

Phase E

322

FORMING BODYPLANS

Rule-set: Hierarchy 1 Pairs: 3 Translation: x+1 Hierarchy 2 Pairs: 3 Rotation: 120 degrees Hierarchy 3 Pairs: 2 Translation: (x+y)+1 Hierarchy 4 Pairs: 3 Rotation: 120 degrees Hierarchy 5 Pairs: 3 Rotation: 270 degrees

In this test we tried both local and collective control simultaneously.

The branch in a small group takes decision to rotate in a certain angle upon wind condition and the angle of bigger groups control to the surface area, speed, and overall orientation.

Perspective View DEPLOYMENT

323

Choreography tests From local rule-sets based on rotation and small displacement Phase A

Phase B

Phase C

Phase D

Phase E

324

FORMING BODYPLANS

Rule-set: Hierarchy 1 Pairs: 4 Translation: (x+y) +1 Hierarchy 2 Pairs: 8 Translation: y+1 Hierarchy 3 Pairs: 6 Translation: x1(2) - x2(0.1)

The type of choreography is more cellular and based on smaller groups turn and orientation by unfolding the tip.

Perspective View DEPLOYMENT

325

breaking threshold

Connections tests

Wind

Target

x

2

[+]

Interaction with wind zones

1

breaking threshold

breaking threshold

a.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

326

FORMING BODYPLANS

The important part about forming bodyplans is that

In these series of tests the composition with the maximum

it happens through interaction between units and

number of connections and the most packed type is the

wind + units with each other. That is why we started

most stable and fast, as (b) shows.

to test different connectivity in different wind zones.

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

327

breaking threshold

Connections tests

Wind

Target

x

2

[+]

Interaction with wind zones

1

breaking threshold

breaking threshold

a.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

328

FORMING BODYPLANS

In tthese series of tests (a) and (d) show that we need

this wind zone. In t=1 and t=2, units couldn’t pass the zone,

certain flexibility to adjust its overall direction of the

so they reconfigured their aggregation while connected

movement in the same choreography to go across

and could get to the target area.

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

329

breaking threshold

Connections tests

Wind

Target

x

2

[+]

Interaction with wind zones

1

breaking threshold

breaking threshold

a.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

330

FORMING BODYPLANS

In these series of tests with a vortex (it happens when

completely change their choreography to cross this type

different wind masses meet) (b) and (d) show that

of wind zone.

the overall strategy of units aggregation needs to

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

331

Adaptive Connections Wind Condition 1 breaking threshold

breaking threshold

Connections tests

breaking threshold

Interaction with wind zones

x

2

1

60

breaking threshold

Wind Zones

a.

30 breaking threshold x

t=1

2

1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

332

FORMING BODYPLANS

2

In these simulations we tried to find the proper

number of connections between them and differernt

number of units, strong enough to stay stable during

breaking tresholds.

strong winds. We simulated bodyplans with different

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

333

Adaptive Connections Wind Condition 2 breaking threshold

breaking threshold

Connections tests

breaking threshold

Interaction with wind zones

x

2

1

60

breaking threshold

Wind Zones

a.

20 breaking threshold x

t=1

2

1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

334

FORMING BODYPLANS

4

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

335

Adaptive Connections Wind Condition 3 breaking threshold

breaking threshold

Connections tests

breaking threshold

Interaction with wind zones

x

2

1

60

breaking threshold

Wind Zones

a.

10 breaking threshold x

t=1

2

1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

c.

336

FORMING BODYPLANS

3

b.

t=1

t=2

t=3

t=4

t=1

t=2

t=3

t=4

d.

Maximum number of connections per unit

DEPLOYMENT

337

Forming bodyplans Attachment/detachment So in a stage that comes after dropping our units start to form specific bodyplans, that optimized for the flight and correspond to the initial calculation based on the site parameters.

The wind serves as a push force for rotation.

The stage happens in between 15000 feet to 12000 feet.

Wind as a push force

Auto-rotation caused by moment of inertia

Wind as a push force

Resultedbodyplan through series of attachment/ detachment

The process of forming bodyplans and the resulted bodyplan after series of attachment/detachment and rotations.

338

FORMING BODYPLANS

23 000 feet Dropped from the airplane

15 000 feet Forming bodyplans

12 000 feet Unfolding the parachute

Manoeuvring

4 000 feet Aggregating

2 000 feet Landing

Land

DEPLOYMENT

339

Forming bodyplans

1 Airplane

Scenarios Scenario 1 Disaster: Wildfire

232 units in 1 airplane/

Location: La Ronge, Canada

464 units in 2 airplanes

People need shelter: 13 000

29 units in one superpack

Wind

340

FORMING BODYPLANS

1 Airplane

Scenario 2 Disaster: Earthquake

232 units in 1 airplane/

Location: Padang, Indonesia

464 units in 2 airplanes

People need shelter: 18 000

29 units in one superpack

Wind

DEPLOYMENT

341

Forming bodyplans

1 Airplane

Scenarios Scenario 3 Disaster: Flood

232 units in 1 airplane/

Location: Lodwar, Kenya

464 units in 2 airplanes

People need shelter: 15 000

29 units in one superpack

Wind

342

FORMING BODYPLANS

1 Airplane

Scenario 4 Disaster: Tsunami

232 units in 1 airplane

Location: Rio de Janeiro, Brazil People need shelter: 9 000

33 units in one superpack

Wind

DEPLOYMENT

343

Unfolding parachute Unpacking by inflating the bottom layer After the bodyplans are formed ,the units unfold from highly packed

state

(diameter

of

one unit 2 m) to unpacked or parachute state (diameter of one unit 15 m). The process of unpacking starts by chemically inflating

the

bottom

layer,

that is vacuumly packed with all other layers. The inflation triggers expansion of the unit and unpacking of everything together. The stage happens in-between 14000 feet to 5000 feet.

Physical experiment: Unpacking by inflation.

346

UNFOLDING PARACHUTE

23 000 feet Dropped from the airplane

15 000 feet Forming bodyplans

12 000 feet Unfolding the parachute

Manoeuvring

4 000 feet Aggregating

2 000 feet Landing

Land

DEPLOYMENT

347

348

08

Manoeuvring o Aerodynamics Research o Manoeuvring strategy *

Manoeuvring - Unit Scale

*

Collective Behaviour

*

Bodyplans Control

*

Proof of Concept

MANOEUVRING

349

Aerodynamics researh Drag: Aerodynamical Form Even after the calculation of the approximate flight

for mass-deployment, we started to look into more passive

path and the dropping point we understand that our

strategies of control. We’ve discovered that the way the

units need to have a certain capacity to manoeuvre

object meets fluid or air resistance can be actually the

because wind can change in real-time.

way to control its flight. So we started to work with fluid

Due to the fact that we incline towards a more

dynamics to understand the relationship between form

probabilistic model; optimizing the cost of shelters

and air resistance or drag.

352

AERODYNAMICS RESEARCH

MANOEUVRING

353

Aerodynamics researh Directing Airflow

The thickness of each cross-section heavily affects overall airflow. The amount of surface that is in contact with the general air flow would determine if the cross section would be more or less aerodynamic.

354

AERODYNAMICS RESEARCH

The angle of each cross section met by the general airflow is also influential guiding the airflow internally within a constructed design.

MANOEUVRING

355

Aerodynamics researh Microturbulence

These tests try to optimize the amount of internal turbulence which a geometric cross-section can include without increasing the total drag too much. Meaning, consecutive air gaps strategically placed so that the whole unit has an understnaing of the threshold to become a parachute.

356

AERODYNAMICS RESEARCH

Balance is also key while trying to control directionality through directing airflow, thus we enede up with test which were more likely to be symmetrical and having a heavier core compared to the rest of the design.

MANOEUVRING

357

Aerodynamics researh Lift: Spinning Logic The

auto-gyration

is

a

characteristic of many longdispersal tree seeds, which causes periodic drag. As the seed glides along a helical path, it generates an aerodynamic force that slows down its descent. The slower descent leads to a longer dispersion under the wind condition, advantageous for the propagation of its species.

Changing direction through weight distribution which is controlled by inflated pattern and models produce lift through specific weight distribution.

358

AERODYNAMICS RESEARCH

MANOEUVRING

359

Aerodynamics researh Centre of gravity: Weight distribution

360

AERODYNAMICS RESEARCH

MANOEUVRING

361

Aerodynamics researh Directing Airflow

362

AERODYNAMICS RESEARCH

MANOEUVRING

363

Aerodynamics researh Weight Distribution tests

Pattern 01: Total Weight : 4 # of Anchors: 4 Surface Area : 1

- Orientation: more or less stable.

- Overall form: did not fully actuate to its full potential.

Unit’s displacement on the XY plane was considerably high and unstable.

Pattern 02: Total Weight : 8 # of Anchors: 4 Surface Area : 1

- Orientation: more stable than Pattern 01.

- Overall form: Due to increase in weight, we started to see a volume being formed unlike Pattern 01.

Unit’s displacement on the XY plane was considerably high and unstable.

364

AERODYNAMICS RESEARCH

MANOEUVRING

365

Aerodynamics researh Weight Distribution tests

Pattern 03: Total Weight : 9 # of Anchors: 9 Surface Area : 1

- Orientation: unstable.

- Overall form: due to the additional central load, subdivisons of volumes were formed.

Unit’s displacement on the XY plane was more stable compared to Pattern 02.

Pattern 04: Total Weight : 6 # of Anchors: 5 Surface Area : 1

- Orientation: stable - Overall form: due to the central load being heavier than the rest, the parachute logic did not work.

Unit’s displacement on the XY plane was considerably little.

366

AERODYNAMICS RESEARCH

MANOEUVRING

367

Aerodynamics researh Weight Distribution tests

Pattern 05: Total Weight : 14 # of Anchors: 5 Surface Area : 1

- Orientation: the intent of guiding the overall fabric to the direction where we have the additional load influenced the entire surface to flip.

- Overall form: the most volume in an iteration we’ve seen so far.

Unit’s displacement on the XY plane is little due to the overall weight being heavier.

Pattern 06: Total Weight : 20 # of Anchors: 8 Surface Area : 1

- Orientation: stable - Overall form: volumetric ratio was close to an actual parachute.

Unit’s displacement on the XY plane was considerably little.

368

AERODYNAMICS RESEARCH

MANOEUVRING

369

Aerodynamics researh Weight Distribution tests

Pattern 07: Total Weight : 24 # of Anchors: 8 Surface Area : 1

- Orientation: more or less stable.

- Overall form: volumetric ratio was close to an actual parachute.

Unit’s displacement on the XY plane is higher.

Pattern 08: Total Weight : 28 # of Anchors: 8 Surface Area : 1

- Orientation: very stable, but also fell the fastest.

- Overall form: actuation happened too close to the ground due to the unit being very fast.

Unit’s displacement on the XY plane was considerably little.

370

AERODYNAMICS RESEARCH

MANOEUVRING

371

Manoeuvring Unit Scale Between

12,000

feet

and

approximately 5,000 feet our units have the manoeuvring stage, where the main goal is to reach the desired location.

It happens by drag distribution in living pods, these cellular spaces in-between two layers.

374

MANOEUVRING STRATEGY

23 000 feet Dropped from the airplane

Wind

Outer Layer parachute layer

15 000 feet

Living Pods control manoeuvring through drag distribution

Forming bodyplans Wind

Bottom Layer

Adaptive Holes

for ensuring stability and unwrapping the parachute behaviour

mechanism that closes/ opens the air channel

12 000 feet Unfolding the parachute

Wind

Manoeuvring

4 000 feet Aggregating

2 000 feet Wind

Landing

Land

MANOEUVRING

375

Collective Behaviour Early speculations But we believe our units should travel in smaller aggregations.

A

This will allow us to achieve greater simpler

control entities,

sophisticated

having performing

tasks

B

through

their interaction and distribute work. In other words some units can remain completely passive,

C

A

A’

B

B’ deflated deflated inflated inflated

optimizing the energy cost. So

inflated*2 inflated*2

we started to speculate how through inflation we can shift the center of gravity with the direction of the flight.

A

B

C

Diagram illustrating the behaviour of agents to control free fall.

Faster Slower

376

MANOEUVRING STRATEGY

A

A’

B

B’

Behaviour rule-set 1.

Diagram of behaviour rule-set 1. : If the velocity is higher than a certain value, it gets inflated.

Slower Velocity Faster Velocity Centre of Gravity

Behaviour rule-set 2.

Diagram of behaviour rule-set 2. : If dropping velocity is too slow, it shows auto-rotation by changing inflated degrees to control centre of gravity.

: If dropping velocity is too fast. It distributes the centre of gravity equally to act as a glider.

MANOEUVRING

377

Collective Behaviour

Early speculations

378

MANOEUVRING STRATEGY

MANOEUVRING

379

Collective Behaviour Bodyplans: types Searching

for

optimal

bodyplans we constructed a framework with abstracted model of the unit only with

A

main components which is the amount of surface for drag and the place of “activated living pods” with physics close to real life and different types of wind. After explorations and different tyials this framework enabled us to choose 4 types

B

of bodyplans, each of them optimized for different weather conditions, having different flight behaviour and its control strategy.

C

D

In case of light wind when there is no need of maneuvering the units can travel in bigger bodyplans

380

MANOEUVRING STRATEGY

Setup1

Setup2

Wind Start

Result

Start

Result

Setup1

Setup2

Wind Start

Result

Start

Result

Setup1

Setup2

Wind Start

Result

Start

Result

Setup1

Setup2

Wind Start

Result

Start

Result

Setup1

Setup2

Wind

Start

Result

Start

Result

Exploration MANOEUVRING

381

Bodyplans control

HELPER

With “Helper” wind (same direction as target)

B

HELPER Controlled opening of living pods for maneuvering by increasing and distributing the amount of drag

OBSTACLE

IN-BETWEEN

Failed macro-patterns:

OBSTACLE ITERATION 1 The distribution resulted in “stall”, which is too unstable and dangerous

IN-BETWEEN ITERATION 2 The distribution created a fracture inside

ITERATION 3 The distribution had too much drag

Actuated living pod

382

MANOEUVRING STRATEGY

Target

Wind Direction

We found the pattern that while being stable create a certain curvature that interacts with the helper wind, reaching the destination faster.

Side view

Perspective from top

Actuated living pod

Target

Wind Direction MANOEUVRING

383

Bodyplans control With “Helper” wind (same direction as target)

HELPER B

HELPER OBSTACLE

IN-BETWEEN

OBSTACLE

Setup

IN-BETWEEN

384

MANOEUVRING STRATEGY

Actuated living pod

Target

Wind Direction

Result

MANOEUVRING

385

HELPER

Bodyplans control

OBSTACLE

With “Obstacle” wind (opposite direction as target)

B

HELPER Controlled opening of living pods for maneuvering by increasing and distributing the amount of drag

IN-BETWEEN

Failed macro-patterns:

OBSTACLE ITERATION 1 Lost internal stability

IN-BETWEEN ITERATION 2 The distribution created a fracture inside

ITERATION 3 The bodyplan is parallel to the wind and this force can result in flipping

Actuated living pod

386

MANOEUVRING STRATEGY

Target

Wind Direction

After trials and errors we found the distribution that has a stable flight with minimum push. However, this particular bodyplan is unable with current control strategy to resist wind and move to the target.

Side view

Perspective from top

Actuated living pod

Target

Wind Direction MANOEUVRING

387

HELPER

Bodyplans control With “Obstacle” wind (opposite direction as target)

OBSTACLE B

HELPER IN-BETWEEN

OBSTACLE

Setup

IN-BETWEEN

388

MANOEUVRING STRATEGY

Actuated living pod

Target

Wind Direction

Result

MANOEUVRING

389

Bodyplans control

IN-BETWEEN

With “In-between” wind

B

HELPER Controlled opening of living pods for maneuvering by increasing and distributing the amount of drag

Failed macro-patterns:

OBSTACLE ITERATION 1 The bodyplan was stable but jut passively followed the wind.

IN-BETWEEN ITERATION 2 The rotation moment achieved but it just followed the wind.

ITERATION 3 The rotation moved the bodyplan to other direction than wind but not where target is.

Actuated living pod

390

MANOEUVRING STRATEGY

Target

Wind Direction

The third wind is in-between which is the third direction, not pushing neither to the target or opposite direction. The main idea here was to create a rotation behaviour that will push to the desire destination.

Creating a specific angle of attack of the surface the wind rotates the bodyplan and moves to the direction of desired destination.

Side view

Perspective from top

Actuated living pod

Target

Wind Direction MANOEUVRING

391

Bodyplans control With “Helper” wind (same direction as target)

HELPER

OBSTACLE

Setup

IN-BETWEEN

392

MANOEUVRING STRATEGY

IN-BETWEEN B

Actuated living pod

Target

Wind Direction

Result

MANOEUVRING

393

Bodyplans control Connections tests

HELPER B

HELPER

We conducted a series of

simulations about how different

OBSTACLE

connections affect the flight in different wind conditions.

Drag distribution IN-BETWEEN

OBSTACLE

POINT

IN-BETWEEN

POINT POINTS

EDGES

394

MANOEUVRING STRATEGY

Actuated living pod

POINTS

Target

Wind Direction

EDGES

MANOEUVRING

395

HELPER

Bodyplans control Connections tests

OBSTACLE B

HELPER

We conducted a series of

simulations about how different

IN-BETWEEN

connections affect the flight in different wind conditions.

Drag distribution

OBSTACLE

POINT

IN-BETWEEN

POINT POINTS

EDGES

396

MANOEUVRING STRATEGY

Actuated living pod

POINTS

Target

Wind Direction

EDGES

MANOEUVRING

397

Bodyplans control Connections tests

IN-BETWEEN B

HELPER

We conducted a series of

simulations about how different connections affect the flight in different wind conditions.

Drag distribution

OBSTACLE

POINT

IN-BETWEEN

POINT

POINTS

EDGES

398

MANOEUVRING STRATEGY

Actuated living pod

POINTS

Target

Wind Direction

EDGES

MANOEUVRING

399

Proof of Concept Behaviours catalogue A

BODYPLAN

Straight

B

Rotate right

Diagonal left

Diagonal right

Rotate left

Rotate right

Diagonal left

Diagonal right

BODYPLAN

Straight

400

Rotate left

MANOEUVRING STRATEGY

Actuated living pod

C

BODYPLAN

Straight

D

Straight

Wind Direction

Rotate left

Rotate right

Diagonal left

Diagonal right

Rotate left

Rotate right

Diagonal left

Diagonal right

BODYPLAN

MANOEUVRING

401

Proof of Concept

Experiment 1.

Experiment 2.

Wind direction Experiment 3.

Experiment 1.

Inflated Deflated

The logic of controlling freefall through drag distribution and stability of multi-layered system we decided to reinforce with physical proofs of concept. First experiment had opened holes only in 2 living pods and it was relatively stable with minimal openings.

402

MANOEUVRING STRATEGY

Wind direction

Experiment 2.

Wind direction

Experiment 3.

In the second experiment we tried a simple non- The third test happened with relatively strong wind symmetrical drag distribution and it proved to work, and all holes opened, the result was a stable landing. we actually saw the displacement.

MANOEUVRING

403

Experiment 1

#1 Dropping

#3 Stabilization 404

Inflated Deflated

#2 Unfolding

#4 Landing

405

Physical Experiment 2

#1 Dropping

#3 Stabilization 406

Inflated Deflated

#2 Unfolding

#4 Landing

407

Physical Experiment 3

#1 Dropping

#3 Stabilization 408

Inflated Deflated

#2 Unfolding

#4 Landing

409

410

09

Landing o Aggregation Phase *

Aggregation Strategies

*

Connecting to Other Units

*

Aggregation

o Landing Phase *

Perception

*

Landing

*

Space Formation by Dropping

*

Freezing the Form

*

Resilience

LANDING

411

Aggregation strategies Early speculations Steep Terrain

In the first strategy, units aggregated to linear branches to react on parameters of both the site and controlling free fall.

In this case the steep terrain dictates more linear or branching strategy of

: control the orientation

aggregating.

Aggregations 414

AGGREGATION PHASE

Phase 01

Phase 02

LANDING

415

Aggregation strategies Early speculations Urban: centralized

In the second strategy, in respond to the ceontralized urban area, it follows the central (water resource) line and other units cluster to it.

Aggregations 416

AGGREGATION PHASE

Phase 01

Phase 02

Phase 03

LANDING

417

Aggregation strategies Early speculations Urban: decentralized

n

Ope

n

Ope

n

Ope

In decentralized urban condition we speculated on having different settlements that are more self-oriented, however by opening the loop they can join with other settlements.

Aggregations 418

AGGREGATION PHASE

Phase 01

Phase 02

Phase 03

LANDING

419

Connecting to other units Perception The decisions with whom to aggregate the units take from the visual sensing and lasers for more precision. We made am experiment with blob tracking algorithm (in this case blue), creating a mask first with HSV filter and then checking how does it work in real-time.

Blob tracking algorithm in ROS with HSV filter mask

420

AGGREGATION PHASE

LANDING

421

Connecting to other units Perception Kinect-like sensors have an ability to get more exact data about the environment and obstacles. Instead of using only camera and image analysis they have a help from lasers.

180 degrees

In this experiment we tested how a

719 lasers

robot (TurtleBot) with the only input data - ability to send lasers in 180 degrees range can manage to operate in unknown environment, avoiding

FINISH

START

obstacles and get out of the maze.

422

AGGREGATION PHASE

Wall detected

from robot_control_class import RobotControl robotcontrol = RobotControl() counter=0 while counter< 70: l = robotcontrol.get_laser_full() maxim=0 array = [l[719], l[540], l[450], l[360], l[270], l[180], l[0]] if array[3] >1: if array[0] < 0.3:

Rotate

print('I sense obstacle on my left side!') robotcontrol.rotate(-15) robotcontrol.move_straight_ time('forward', 0.3, 1.5) elif array[6] < 0.3: print('I sense obstacle on my right side!') robotcontrol.rotate(15) robotcontrol.move_straight_ time('forward', 0.3, 1.5)

Wall detected

else: print('move forward!') robotcontrol.move_straight_ time('forward', 0.3, 1.5) else: left = array[0]+array[1]+array[2] right = array[4]+array[5]+array[6] if left < right: print('rotate right!') robotcontrol.rotate(-60)

Rotate

else: print('rotate left!') robotcontrol.rotate(60) counter+=1 Elegant code for operating in the environment based on the only availaible sensory data

Test with TurtleBot Robotget and the maze in Gazebo simulation environment in ROS

LANDING

423

Connecting to other units Attaching through changing the drag distribution The simulation shows how the units attach through controlling the distribution of drag, reaching each other from different heights and controlling the speed of fall.

424

AGGREGATION PHASE

Actuated living pod

LANDING

425

Aggregation Unit to unit recognition

426

AGGREGATION PHASE

23 000 feet Dropped from the airplane

t=0

t=1

15 000 feet Forming bodyplans

12 000 feet t=2

Unfolding the parachute

Manoeuvring t=3

4 000 feet Aggregating

2 000 feet t=4

Landing

Land

LANDING

427

Aggregation Combinatorial possibilities

Bodyplan 4 BODY A + C

Bodyplan 1

Bodyplan 2

BODY A + B + D

BODY C + D

Bodyplan 3 BODY C + D

Diagram showing bigger bodyplans made of various body types.

1. Body A + Body B + Body D 2. Body C + Body D 3. Body C + Body D 4. Body A + Body C

428

AGGREGATION PHASE

Bodyplan 1_ Body A + B + D

Bodyplan 2_ Body C + D

Bodyplan 3_ Body C + D

Bodyplan 4_ Body A + C LANDING

429

Perception Self-Localization We tested an unsupervised learning land

algorithm cover

for

classification,

distinguishing which locations are safe to land and analysing the ground. Based on these parameters and GPS data units make a decision to land.

The plan that is generated in

Maps produced in ArcGIS

pre-deployment part divides the map into clear landing, where it is safe to land, emergency landing, where it is also possible to land and

Location: Lodwar, Kenya

Location: Padang, Indonesia

Location: La Ronge, Canada

Location: Rio, Brazil

no landing, zones that units should avoid. Acquiring data from cameras and GPS, the units try to localize themseles, comparing pixels of the realtime data with embedded one.

Sub-pixel localization. Credits: Koen J. A. Martens, Arjen N. Bader, Sander Baas, Bernd Rieger, and Johannes Hohlbein

432

LANDING PHASE

LANDING

433

Perception Self-Localization

Landing 1. Safe Landing

Manoeuvering

•

Land Cover Classification (Image Analysis)

1. Environment

•

Open Field Selection

•

•

Lasers for obstacle detection

Wind data

2. Localization

2. Environment

•

Altitude

•

Computer vision (Image Analysis)

•

GPS (coordinates)

•

Wind Data

3. Self-Modelling (information about units

3. Self-Modelling (information about units themselves and

themselves and attachment to others)

attachment to others)

•

Attachment/detachment sensors

•

Attachment/detachment sensors

•

Air Pressure sensors

•

Air Pressure sensors

Land Cover Classification

Open Field Selection The field for landing

Moving objects recognition Moving obstacles detection such as cars or humans

Land Use Classification Terrain analysis and Land use classification.

Objects recognition Obstacle detection such as buildings, infrastructure, trees

434

LANDING PHASE

Computer Vision/ Image Analysis Object detection/ recognition To recognise and detect objects is one of the parameters for safe landing, espcially the moving ones such as cars or people. In this experiment we tested an algorithm that detects human face and recognise it, comparing to existing library with machine learning algorithm in real-time.

Face Detection and recognition test ROS - Robot Operating System

LANDING

435

Landing Stability/Impact

More drag/ Less impact force during landing

We conducted a series of aerodynamic

tests

around

stability while landing and the impact force.

If the weight distribution of the parachute in the corners is not

Less drag/ More impact force during landing

correct ( iteration 2) the flight is wobbly and unbalanced. For stable arrival, there is a need in internal tension or heavier edge line (iteration 5).

The

impact

showed

that

force

studies

the

more

aerodynamic form is (drops faster) or heavier - more impact force it has in the moment of landing. However faster landing also means more controllable in terms of location if it is initiated in the right moment according to correct self-localization.

Terminal velocity

Iteration 1 force during landing Less drag / More impact

436

LANDING PHASE

Iteration 2

Iteration 3

Iteration 4

Iteration 5 Parachute stability during landing Dropping control strategies. Drag control.

LANDING

437

Landing Locking the air and releasing anchors Closer to the ground (between

CROSS-REFERENCE TO GPS DATA

4000 to 1000 feet) the units land, after sensing the suitable location, locking the air inside of living pods and releasing anchors. Anchors take the

AM I IN THE RIGHT LOCATION?

YES

impact force, saving the

NO CALCULATE DISPLACEMENT

materials and also secure the settlement on land.

GENERATE DISPLACEMENT VECTOR LOCK THE AIR GAPS

INFLATE PODS ALONG GENERATED VECTOR

CALCULATE RELATIVE VELOCITY STABLE

UNSTABLE

RELEASE ANCHORS

CLOSE THE HOLES

CHECK CONTACT WITH LAND CALCULATE IMPACT FORCE

Anchors, released just before landing to secure the units and take the impact force.

438

LANDING PHASE

SMALL

BIG

DESCEND FASTER

DESCEND SLOWER

23 000 feet Dropped from the airplane

t=1

15 000 feet Forming bodyplans

t=2

12 000 feet Unfolding the parachute

Manoeuvring

t=3

4 000 feet Aggregating

2 000 feet Landing

t=4

Land

LANDING

439

Space formation by Dropping Changing the weight of anchors, modifying overall typology

ITERATION 1

Heavy

Medium

Light

Heavy

Medium

Light

ITERATION 2

Connections

440

LANDING PHASE

Heavy

Heavy Heavy

Medium Load Weight

Medium

Light

Light

Airflow

The simulation shows how the different weight patterns will result in architectural formation. The points inside has different weight load.

By changing their location and weight the morphology can change considerably.

Weight Load

ITERATION 3

Heavy

Medium

Light

Heavy

Medium

ITERATION 4

Heavy

Heavy

Heavy Medium Medium

Medium Weight Load Light

Medium

Light

Light Light

Weight Load LANDING

441

Space formation by Dropping Changing the weight distribution and drag distribution

ITERATION 1

ITERATION 1

ITERATION 1

442

LANDING PHASE

In contrast to previous tests, here we experimented not only with

More drag

Airflow

weight distribution but also drag distribution as opposite force. The resulted morphologies have more radical morphological change.

Weight Load

Heavy

Medium

LANDING

443

Space formation by Dropping Anchors as another driver of resulted morphologies We introduced anchors that are coming from

Because each aggregation land in different shape,

selected columns during landing as a mean to take

resulted from activated living pods, the impact

most of the impact force, saving the material of

force is diffferent for different anchors. The different

the unit from breaking, and to act as a basement,

impact force, resulted from different speed of falling

protecting the settlement from unwanted sliding

of parts can be another morphologycal driver,

caused by strong winds.

translating the flying form to the one on the ground.

The bodyplan has a pecific shape while landing in low altitude resulted from aggregating and maneuvering before that.

The specific columns eject anchors close to the ground. (mostly around the corners and some in the midddle of aggregation)

The anchors that were coming faster, have a bigger impact force and end up lower and vice versa.

Column

444

LANDING PHASE

Ground

Anchors

LANDING

445

Space formation by Dropping Anchors as another driver of resulted morphologies

446

LANDING PHASE

LANDING

447

Freezing the From Chemical Inflation Baking Powder + Citric Acid In

this

experiment

we

combined cheap and nontoxic

ingridients

such

as

citric acid and baking soda in order to release the chemical potential

energy

between

acid and base into inflateable carbondioxide. A gas, nonflammable and more dense than air. Based on that we started to design the chemical capsules meachanism that support structures.

our

air-inflated

The

capsules

can be done in any place of the world, don’t require specialized manufacturing.

Freezing the form After the aggregation took a decision to land, the units close the holes locking the air inside. Because of the smart textile with rigidity patterning, when the inflation happens by chemical reaction of the walls, the space and size stays the same as it was inflated in the air. This interrelationship

Chemical reaction

Produced inflation

actually preserves the built morphology in the air.

Chemical reaction between baking soda, citric acid and water.

448

LANDING PHASE

Closing holes and locking the air

Chemical Inflation that keeps the form inflated in the air

Water storage

Water to dissolve chemical capsules and start chemical reaction

Connected to water collectors

Dissolvable packet Chemical Capsules

Possibility to add more capsules after landing

Chemical inflation of Air Pockets (CO2) Citric acid crystals

Baking powder

Chemical capsules mechanism LANDING

449

Freezing the Form Landing moment as a morphology guide on land

450

LANDING PHASE

The digram of pods placement in 1 unit LANDING

451

Resilience Inner Protocols to deal with unexpected difficulties In case something goes wrong such as malfunction

this situation. Or if there is a certain malfunction,

of specific units, wind change or difficulty with

units can try to rearrange themselves and land with

landing in target location the units should have

it safely. If the wind is too strong the best strategy

inner protocols how to deal with these situations in

the units can take is to dissipate. In case of any

the best possible way. For example, if close to the

difficulty to aggregate with unit that was calculated,

landing site the units find themselves in no-landing

the units can attach simply to the closest ones.

zone, they shoudld have clear steps how to deal with

Landing Location

Clear Landing

452

LANDING PHASE

Emergency Landing

No Landing

Malfunction of Specific Units Scenario 01

Rotation

Unit goes in the middle

Scenario 02

Disconnect the Unit

Adapt the Shape

Wind Condition Mild Wind

Stronger Wind

Very Strong Wind

Large Body

Small Body

Plan

Plans

Individual Units

Aggregation

Flexibility of Aggregation According to Location or Wind Parameter

LANDING

453

454

08

Land Operation o The Settlement Model o Self-Sustainability *

Directing Rainwater

*

Harvesting Water

*

Climate Control

*

Self-Sufficiency

o Adaptation *

Population changes

*

Combinatorial possibilities

*

Scale of Living Pods

o Cellular Living o Long-Term Adaptation

LAND OPERATION

455

The Settlement Model Adapting to the landing site

Terrain Type 1

100 Units

t=1

The existing aerial image with the raster analysis is broken down into zones which are either safe or dangerous for landing. In this example as the terrain

t=2

condition gets more flat, the output would end up in a white pixel whereas; if the terrain condition is either steeper or covered with obstacles such as forestation or large bodies of water the output would end up in a black pixel. As for the system, while it makes its decision where to land our units it assumes that the area of freedom is considerably larger in areas where it is more likely to land safer.

458

THE SETTLEMENT MODEL

t=3

Terrain Type 2

Terrain Type 3

75 Units

100 Units

LAND OPERATION

459

The Settlement Model Adapting to the landing site

10th Offspring

1000th Offspring So in these examples we see how the agents pack and adjust themselves according to the given environment. From this topology of connections the system is capable of constructing a network of points; which will in the end, be the base for the circulation in this network.

1001th Offspring

A pathfinding algorithm is applied in order to determine the shortest distance possible between two points of entry. The resulting distance would be used as a function of fitness in an evolutionary algorithm that would try to minimize this length.

460

THE SETTLEMENT MODEL

1002nd Offspring

Terrain Type 1 100 Units

Terrain Type 2

Terrain Type 3

75 Units

100 Units

LAND OPERATION

461

The Settlement Model Adapting to the landing site

100 Units

A flow calculation is also added to the settlement placement because of the surface and the boundary conditions that the system creates above itself are to be used for locations of

t=1

potential artificial basins around the settlement to store rainwater.

t=2

t=3

t=4

462

THE SETTLEMENT MODEL

Terrain Type 1

Terrain Type 2

Terrain Type 3

75 Units

100 Units

LAND OPERATION

463

The Settlement Model

Example 1

Additional pods inflation

100 units

Example 1 100 units

As a result, when we add up all the given parameters; we can

Example 21 Terrain Type

Example 1 100 100 unitsUnits

Example 2 100 100units units

ment. In these examples we see

1st Iteration

1st Iteration

1st Iterat

2nd Iteration

2nd Iteration

2nd Itera

2nd Iteration

2nd Iteration

2nd Itera

3rd Iteration

3rd Iteration

3rd Itera

3rd Iteration

3rd Iteration

3rd Itera

1st Iteration

2nd Iteration

vate unit has the capability to activate into a public one. The conditions of this transformation are linked with : Proximity to an artery, number of neighbors within a certain distance, and

system determines where to apply this transformation.

464

THE SETTLEMENT MODEL

100 units

1st Iterat

ther public or private. Each pri-

all of these conditions meet the

Exampl

1st Iteration

two types of units which are ei-

proximity to a water basin. When

100 units

1st Iteration

start to talk about certain programmatic syntax of the settle-

Exampl

3rd Iteration Iteration 1-2-3

Terrain Type 2

Example 2 100 Units

100 units

Terrain Type 3

Example 3 100 Units

100 units

1st Iteration

1st Iteration

2nd Iteration

2nd Iteration

3rd Iteration

3rd Iteration

Iteration 1-2-3

Iteration 1-2-3

LAND OPERATION

465

The Settlement Model Circulatory network with evolutionary computing 1.

Circulatory Network: From this topology of connections the system is capable of constructing a network of points.

It will become a foundation of the circulation network. The resulting distance is used as a function of fitness in an evolutionary algorithm that would try to optimize this length.

1.

Distance with neighbour to be taken into consideration.

2.

Eavluation of the boundary of each dwelling unit.

466

THE SETTLEMENT MODEL

2.

3.

4.

LAND OPERATION

467

The Settlement Model Programs and circulation

Circulatory Network: Different types of natural disasters may require different types of programs, but overall we designed our settlement to have housing with water retention units or where people actually live, agriculture, and hospitals in cases where we have high numbers of wounded people.

Circulation

468

THE SETTLEMENT MODEL

Basin Density

Neighbours

“Dwelling” Circulation: Directional Pod Placement: Apparent Intersections

“Agriculture” Circulation: Maximum Pod Placement: Open Plan

“Caring” Circulation: Maximum Pod Placement: Private

LAND OPERATION

469

The Settlement Model Programs and circulation The program is distributed using evolutionary computing, being able to densify housing areas to the maximum, but keeping the circulation network according to the boundary conditions of each living pod.

Methodology Process:

470

THE SETTLEMENT MODEL

Option B Hot climate– High Terrain

Option C Hot climate– High Terrain

Option D Hot climate– Flat Terrain

LAND OPERATION

471

The Settlement Model Examples

Hot Climate / High Terrain 67 percent Open Plan

Hot Climate / High Terrain 48 percent Open Plan

Hot Climate / High Terrain 35 percent Open Plan

Hot Climate / High Terrain 25 percent Open Plan

472

THE SETTLEMENT MODEL

Cold Climate / Flat Terrain

Central Public Programmatic

a.

b.

c.

d.

e.

f.

Pod Inflation Level:

LAND OPERATION

473

The Settlement Model Examples Programmatic: Pod density distribution is customized according to environmental parameters such as landed state, program, terrain, climate, and circulation.

Hot Climate / Flat Terrain

474

THE SETTLEMENT MODEL

a.

b.

c.

d.

e.

f.

Hot Climate / High Terrain

a.

b.

c.

Pod Inflation Level:

d.

e.

f.

LAND OPERATION

475

The Settlement Model 1. Moment of Dropping: 150 units

Illustrating it in an example of the settlement for 11 000 people, 5850 families. On the right are the pods actuated by dropping and on the right additionally inflated ones with water harvesting units calculated. The small pods on the corner of each unit can be detached and joined with other units to create apart-

476

THE SETTLEMENT MODEL

LAND OPERATION

477

The Settlement Model 2. Additional inflation Additional Possible Inflations: Total Housed: 11000

478

THE SETTLEMENT MODEL

LAND OPERATION

479

The Settlement Model Circulation

The circulation plan and possible programmatic division of the example of the settlement. For housing, the system calculates a variety of co-living spaces for different sizes of families. The pods can even create zones with small courtyards in between for larger families.

480

THE SETTLEMENT MODEL

LAND OPERATION

481

The Settlement Model Programs

The circulation plan and possible programmatic division of the example of the settlement. For housing, the system calculates a variety of co-living spaces for different sizes of families. The pods can even create zones with small courtyards in between for larger families.

482

THE SETTLEMENT MODEL

LAND OPERATION

483

The Settlement Model Timeline

Scenario 01 Kenya Hot Climate / Flat Terrain

484

THE SETTLEMENT MODEL

Scenario 02 Canada Cold Climate / Flat Terrain

LAND OPERATION

485

The Settlement Model Timeline

Scenario 03 Indonesia Hot Climate / High Terrain

486

THE SETTLEMENT MODEL

Scenario 04 Brazil Hot Climate / High Terrain

LAND OPERATION

487

Directing Rainwater Noise Pattern 01: Transforming surface formations according to the flow parameters is what we tried to achieve. As the noise pattern increases in resolution it is possible observe more ways to distribute collected rain-water.

Parameter 01. Simplex Perlin Parameter 02. Bevins Gradient

490

SELF-SUSTAINABILITY

LAND OPERATION

491

Directing Rainwater Noise Pattern 02: By adjusting the weight of each patter output we were able to create branched divisions to make more aggressive and linear flow path into more centric and shareable flow lines.

Parameter 03. Scale Parameter 04. Billow

492

SELF-SUSTAINABILITY

LAND OPERATION

493

Harvesting Water

01

02

02 a

02 b

02 c

494

SELF-SUSTAINABILITY

03

04

04 a

04 b

04 c

03 a

03 b

03 c

01 a

01 b

01 c

LAND OPERATION

495

Harvesting Water The relationship between the pods and outer layer

496

SELF-SUSTAINABILITY

Affect the outer layer only if they are large,managing local water harvesting

Affect the outer layer direclty,managing glcoal water harvesting

Don’t affect the outer layer, can be moved to join with others

LAND OPERATION

497

Harvesting Water The relationship between the pods and outer layer

Top View

Top View

Top View

498

SELF-SUSTAINABILITY

Isometric

Isometric

Isometric

Iteration 01: Number of Water Retention Units : 1 Number of Family-Sized Living Pods : 1 Number not Activated Living Pods: 9

Isometric

Iteration 02: Number of Water Retention Units : 2 Number of Family-Sized Living Pods : 0 Number not Activated Living Pods: 14

Isometric

Iteration 03: Number of Water Retention Units : 1 Number of Family-Sized Living Pods : 2 Number not Activated Living Pods: 7 Isometric LAND OPERATION

499

Harvesting Water

500

SELF-SUSTAINABILITY

LAND OPERATION

501

Climate Control The heating happens by distributing heat from the

Because the chemical reaction that is used to inflate the

kitchens. For this reason in cold climate the units

air pockets of the units is endothermic (it absorbs heat

surface to preserve

from the atmosphere) we control the temperature inside

Kitchen areas with public

of the aggregation by continuously inflating columns

gathering are dispersed aroung the settlement in order

and skin. Another strategy is in the logic of aggregating,

Land Operation aggregate in a closed airtight Adaptive andheat flexibleas shelters as much possible.

Climate Control

Deploym

Air Dropped s emergency sh Natural Disast

to enable this strategy.

which is creating inner courtyards to enable the wind

Land Op

The heating happens by distributing heat from the kitchens. For this reason in cold climate the units aggregate in a closed airtight surface to preserve as much heat as possible. Kitchen areas with public gathering are dispersed aroung the settlement in order to enable this strategy.

Because the chemical reaction that is used to inflate the air pockets of the units is flow. endothermic (it absorbs heat from the atmosphere) we control the temperature inside of the aggregation by continuously inflating columns and skin. Another strategy is in the logic of aggregating, which is creating inner courtyards to enable the wind flow.

Emergency sh settlements th comfortable t for those who affected by Na

Cold climate

Hot climate

Heat coming from the kitchens that is distributed

126 units

126 units

Endothermic chemical reaction that inflates the columns and cools surroundings

Inner courtyard

Part of th

Column

After the hous and city is hea normal life, th (gravitectons) play the role i Former shelte objects of infr greenhouses, and museums alternative so and water to t

Kitchens and public gathering area

Living pods connected

502

SELF-SUSTAINABILITY

Kitchens and public gathering area

Real-time Adaptation

A.

Kitchens & Public Gathering Area

Connected Living Pods

Inner Courtyard

Kitchens & Public Gathering Area

A.

B.

B. LAND OPERATION

503

Climate Control Self-Sufficiency Climate control is another

t=1

strategy for self-sufficiency we look into. Because the source of our additional inflation is endothermic chemical reaction – which means absorbs heat from the atmosphere - we can change the inflation rate more often getting sort of decentralized passive air conditioning system.

For cold climate the heat can be distributed from the shared kitchens that are placed strategically. t=2

t=3

01

504

SELF-SUSTAINABILITY

t=1

t=2

t=3

t=4

02 LAND OPERATION

505

Climate Control

t=1

t=2

t=3

03

506

SELF-SUSTAINABILITY

t=1

t=2

t=3

04 LAND OPERATION

507

Self-Sufficiency

Warka Towers, Arthuro Vittori.

Energy Generation During Air Dropping phase the generators inside of pillars generate

electric

by

and

falling

energy

contracting

the airflow. Essentially the potential gravitational energy is converted to kinetic by Wind dropping and kinetic is converted to electric with the help of turbine and generator.

The generated electric energy enables the system to control

the localization during free fall.After the shelters land, the stick that holds the turbine can be extended and the electric energy will be generated by winds and rains.

508

SELF-SUSTAINABILITY

Water Collection After Natural Disasters there is a big chance that infrastructure is copromised so it is crucial for the settlements to be selfsus tainable .C olle c te d

rainwater, dehumidifiers from living pods and fog catcher nets provide drinking water and water for other needs.

These showering

needs

include

and

enabling

chemical capsules for inflation.

Dehumidifiers also help to ensure a safe environment in living pods decreasing the risk of indoor mold.

LAND OPERATION

509

Population Changes Population in shelter settlements is not a constant.

As a result flexibility is essential for settlements to be able to adapt over time.

Canada Total Affected: 5,000

Iteration 01

Iteration 02

Iteration 03

Min

Max

1-2 People

5-6 People

512

ADAPTATION

Brazil Total Affected: 15,000

Iteration 01

Iteration 02

Iteration 03

Min

Max

1-2 People

5-6 People

LAND OPERATION

513

Population Changes Population in shelter settlements is not a constant.

As a result flexibility is essential for settlements to be able to adapt over time.

Indonesia Total Affected: 250,000

Iteration 01

Iteration 02

Iteration 03

Min

Max

1-2 People

5-6 People

514

ADAPTATION

Kenya Total Affected: 12,954

Iteration 01

Iteration 02

Iteration 03

Min

Max

1-2 People

5-6 People

LAND OPERATION

515

Combinatorial Possibilities Different appartments

Population 1 person housings

Population 2-4 persons housings 1 person housings Mixed

Population 2-4 persons housings 1 person housings 4-6 persons housing Mixed 2-4 persons housings 4-6 persons Public units housing Mixed Public units 4-6 persons housing Water Harvesting Public units Public Harvesting Space Water

Public Space Water Harvesting

Public Space

With the different scale of pods, the

choreography

creates

different zone planning.

The public space consists of the water harvesting space which can be both the public open space

supporting

a

shared

kitchen and the emotive space using harvested water and light to bring a long-term space for human well-being.

516

ADAPTATION

LAND OPERATION

517

Scale of Living Pods Different spatial typologies

518

ADAPTATION

Pod Inflation Level:

LAND OPERATION

519

Scale of Living Pods Controlling the scale with inflation rate and rigidity pattern

Combining rigid material (red) with elastic (blue), we are able to transform a deflated column, into the biggest living pod, where the ratio of space over material is in its maximum.

The elasticity level can be also related to the transparency of materials, private - opaque or windows - transparent.

520

ADAPTATION

LAND OPERATION

521

Scale of Living Pods Controlling the scale with inflation rate and rigidity pattern

t=1

t=2

522

ADAPTATION

t=3

t=4

LAND OPERATION

523

Scale of Living Pods Controlling the scale with inflation rate and rigidity pattern

524

ADAPTATION

LAND OPERATION

525

Cellular Living Components of Living Pods

Water Retention Compartment Rigid Fabric

Rigid base Inflateable Membrane

528

CELLULAR LIVING

Harvested Water from Net

Deflated

Inflated

LAND OPERATION

529

Cellular Living Adaptive Furniture

Ceiling

Inflatable Tiles Dwelling in a living pod is supported by inflatable tiles of the pod located on the floor.

Inhabitants inflate different numbers of tiles depending on their needs. For example, one tile can be actuated for a chair and three tiles can work as a bed.

Gate

Window Inflatable Tiles on the Floor

530

CELLULAR LIVING

S, M, L There are three types of scale of pods: S, M and L, as the result of the negotiation of maneouvring.

To control that, the patterning of the rigidity on the ceiling of the pod decides the degrees of spatial actuation.

Chemical Inflation Capsule The adaptibility of living is performed by chemical inflation, which removes the need for heavy machinery and makes spatial actuation autonomous, light and cheap through chemical reaction between baking soda, citric acid and water.

Storing them in specialized capsules, the inflation happens by the intentional mixing of those. With a designed system of controlled tubes embedded in the structure, the inflation can be guided to different scale of space out of air.

LAND OPERATION

531

Cellular Living Adaptive Furniture

532

CELLULAR LIVING

LAND OPERATION

533

Long-Term Adaptation Participating in City Recovery

Disaster

Crisis

Deployment

Land Operation

Air Dropped settlements for

Emergency shelter settlements

emergency shelters after Natural

that provide comfortable

Disaster occured

temporary stay for those whose housese were affected by Natural Disaster

536

LONG-TERM ADAPTATION

Recovery

Normality

Part of the city After the houses are rebuilt and city is heading towards normal life, the units (gravitectons) will continue play the role in city’s life . Former shelters will become objects of infrastructure, greenhouses, park pavillions and museums, providing alternative source of energy and water to the city.

LAND OPERATION

537