Orb[i]s

Orb[i]s | AADRL

Thesis | 2018-2020

To our Families

Contents

Preface

12

Research

18

Initial Tests

36

Unit Development

62

System Aggregation

82

Structure Formation

124

Behavior

146

Collective Intelligence

176

Prototyping

198

Illumination Strategies

240

Bibliography

271

Orb[i]s | AADRL

Thesis | 2018-2020

Preface

Preface Introduction

“Without culture, and the relative freedom it implies, society, even when perfect, is but a jungle.

This is why any authentic creation is a gift to the Future.�

- Albert Camus

Culture A cultural space is not defined by a physical entity; rather it’s a metaphorical expression for communication and sociability. It is the communication that constructs meanings of various places.

These spaces play a vital role in shaping a city, with the profound impact it has on its inhabitants.

City A city in the age of constant change and development is an ideal site of cultural diversity. With constant changes in the economy, development, job opportunities and growth, cities have a large influx of people from varied backgrounds, contributing to a wide spectrum of cultural expectations. Cities also tend to a large floating population that adds to the constant change. In order to promote and protect cultural interests, an adaptive system should be used in the creation of cultural spaces that can constantly address this latency and change.

Our vision Over the decades, as art has resonated culture, drawing an important link between humans and culture; we envision the creation of cultural spaces that synthesis art, technology and culture, encouraging use today as well as in the years to come.

Preface

Studio Brief

Constructing Agency: Future Culture 3.0 In order to address the Future of Culture, architecture must be explored through the lenses of mobility, transformability and self-structuring systems. Our work will traverse the path of adaptive forms that can reorganize and be autonomous to move beyond form. We envision the creation of a realm that has its own predispositions and will be choreographed by self-assembling and structuring systems that will curate spaces beyond conventional building typologies.

The Spyropoulos studio explores how behavior-based design methods can be used to reconsider cultural projects for today, through the development of self-aware practices that see architecture as an infrastructure to address latency and change. This infrastructure would be intelligent and machine learned in order to support its adaptive attribute and cater to the environment it is set in.

Our intent is to develop new relations in the ever evolving and adaptive human-machine ecology.

Orb[i]s | AADRL

Thesis | 2018-2020

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Statement Thesis Statement

Orb[i]s is a Design – Research Project aimed at addressing the constant change in urban environments, with need for urban infrastructure that adapts to this constant change.

Therefore, Orb[i]s is a proposal for a Prototypical System that is highly adaptive in nature, creating spatial infrastructure in the urban environment that will respond to the changing needs, numbers and development within the city, augmenting daily experiences and activities.

Orb[i]s is a behavioural assembly that opens up the possibilities of a dynamic environment that is not limited to a building plan, rather is autonomous, adaptable, dynamic and self–assembling based on real-time data culminating in a constantly reconfiguring ecology.

A sensory system encourages decision making in the system, making it self-aware. The self-assembling quality of the system arises from unit to unit communication, leading to higher orders of organizations and structures.

The smallest scale of the system, being the unit itself, has the ability to be mobile, sense, and make decisions. Upon coming together with other units, the system demonstrates collective intelligence and plethora of changing behaviours, providing a higher level of flexibility and adaptability.

Preface

The highly adaptive nature of the system springs from the ability of a single unit’s mobility with the addition of a multitude of such units; As well as transform-ability at the unit level, with its ability to telescopically extend and connect to other units. These connecting arms bring about considerable transformation within an existing structure, without the need for more units.

In addition to its adaptive, space-making ability, the system provides a layer of interaction with its users. Culminating from research of light and its various effects, illumination has been incorporated in the system to provide an added layer of experience. The ambience created by the play of light and shadow, though not physically yet brings about transformation in the space generated.

In all, our ecology is envisioned to provide spatial and experiential infrastructure, augmenting and aiding urban change.

Orb[i]s | AADRL

Thesis | 2018-2020

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Research

Introduction

London as its been from back in the day to today, has seen nothing but constant change.

A city like London, has always been extremely diverse, fast paced and an ever changing metropolis with constant development and need for infrastructure.

With such diversity and contrasting factors, catering to the varying desires of large masses is always a task.

Also keeping in mind the drastic weather patterns which further adds to the ordeal successful of social and cultural engagement.

The most popular trend and desire means of engagement in a city so diverse is through art and culture.

Art based activities are always drawing masses and promoting engagement.

Although, gaining access to such engagement is not always viable.

Most of events like these are highly labor intense, specific to particular locations and time specific.

What we need, is a means of catering to these ever changing needs.

What we need is a system, that will not only adhere to these changing factors but cope with and enhance experiences through time.

A system that will aid and enhance daily experiences whilst addressing the change and latency that surrounds us.

Future of Culture.. Orb[i]s

Research

Orb[i]s | AADRL

Thesis | 2018-2020

20

Case Studies Self Organizing Systems Social Insects : Behavioural Design Strategies The design of the Orb[i]s system, is inspired by the behaviour of social insects. Just like a colony of social insects where individual activities do not need a supervisor, yet they achieve a high degree of organization. Self-organization emerges out of interactions among individuals with a simple set of behaviours.

Important features of self-organization are the flexibility and the robust means of adaptation, allowing for efficient performance of a system.

The path to problem solving in these systems aren’t predetermined, they are rather emergent and result from interactions amongst individuals and their environment.

Patterns of self-organization are generally based on nearest neighbour interaction. In all, a self-organizing system is one which is dynamic, where structures appear at the global level of a system from interactions at lower level components.

Based on these design strategies, our system will use a similar approach in enabling autonomous behaviour in each Orb[i]s. Using a Bottom-Up approach, the system will a culminate into a multi-agent system from simple unit-to-unit interaction.

Research

Kilobots : A thousand Robot Swarm Swarm Intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. Examples of systems studied by swarm intelligence are multi-robot systems and certain computer programs that are written to tackle optimization and data analysis problems.

The Kilobot swarm is a thousand-robot (1024) swarm designed program and experiment with collective behaviours in large-scale autonomous swarms.

Each robot is equipped with the basic capabilities required for an autonomous swarm robot (programmable controller, basic locomotion, and local communication), but is made with low-cost parts and is mostly assembled by an automated process.

Additionally, the system design allows the user to easily scale and operate larger Kilobot collectives. The Kilobot swarm was used to investigate collective artificial intelligence (e.g. sync, collective transport, self-assembly) as well as to explore new theories that link minimal individual capabilities to achievable swarm behaviours. By using a combined theory-experiment approach, the aim was to develop new algorithmic insights into robustness, scalability, self-organization, and emergence in collectives of limited individuals.

Self Assembly Algorithm

Orb[i]s | AADRL

Thesis | 2018-2020

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Case Studies Interactive Systems Light Pollination : Illumination - Response Systems Light Pollination is an interactive digital artwork commissioned by iGuzzini to celebrate the power of light as a vehicle for social innovation and was designed by London-based digital-arts studio UniversalAssemblyUnit.

The installation seeks to spread the word about light, and in doing so, it explores the strong links between light and communication. Fiber optic, the primary material used to create the artwork, is a vehicle for light through which high-speed communication is facilitated.

Thus, the art installation is both an expression and a prototype of this, albeit on a smaller scale. Rather than addressing a particular function, it imagines an alternative way of interacting with artificial light.

Research

Fluid Assembly Chair : Self Assembly Lab, MIT Fluid Assembly is part of a series of investigations by MIT’s Self-Assembly Lab looking at autonomous assembly in complex and uncontrolled environments (water, air, space etc).

In this experiment a number of components are released into a tank of turbulent water.

Each of the components is completely unique from one another and has a precise location in the final structure. The process was filmed over 7 hours, after which a full assembled, precise chair was created.

The chair was selected to demonstrate differentiated structures as opposed to repetitive growth or self-similar structures. This experiment points towards an opportunity to self-assemble arbitrarily complex differentiated structures from furniture to components, electronics / devices or other unique structures.

Once self-assembled, the structures can be removed, tested, used or disassembled and thrown back into the chamber.

Orb[i]s | AADRL

Thesis | 2018-2020

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Research Colour and Lights The Subconscious Effect of Coloured Lighting Keeping with the latter theme of our vision, we wish to integrate illumination in our system.

Illumination is envisioned to add a layer of experience and utility to the system as a whole.

Light creates more than just visual effects (image, shape, intensity, perception, contrast, etc.); it also has biological and psychological effects that can impact the health and wellbeing of humans.

Brightness, hue and saturation are the three main qualities of light in relation to colour.

Brightness is the amount of light given off by a light source, usually expressed in lumens or lux. Some studies have shown that brighter light can intensify emotions, while low light doesn’t remove emotions, but keeps them steady.

Hue is defined as a colour or shade. Colours created by artificial light can evoke different emotions and have effects on the human body.

Saturation is the intensity of a colour. More saturated hues can have amplifying effects on emotions, while muted colours can dampen emotions. In art, saturation is defined on a scale from pure colour (100% [fully saturated]) to grey (0%). In lighting, a similar scale can apply.

Colour subconsciously effects our everyday life. It is used strategically to elicit a specific feeling in people. While each colour has a separate meaning, they also each have a separate feeling associated with them, affecting our mood in everyday situations.

Research

IMPACT OF COLOURS GREEN: is known as the strength provider. In lighting green can be used to portray nature, growth, cool, money, health, envy, tranquility, harmony, calmness, fertility, safety and ambition.

BLUE: is known as the bringer of peace. Blue can be used to portray trust, loyalty, cleanliness, air, sky, water, health, tranquility.

PURPLE: can help reduce emotional and mental stress, it portrays royalty, power, creativity, mystery.

RED: can be used to portray love, warm, intensity, passion, danger, leadership, courage and friendship.

ORANGE: known as the source of creativity, orange stimulates the creative thought process and can help people come up with new ideas.

YELLOW: can sometimes be beneficial in the treatment for depression. In lighting design, yellow can be used to portray happiness, laughter, cheer, warmth.

WHITE: can be used to portray purity, innocence, cleanliness, sense of space, neutrality, safety, beginning, faith and coolness.

Orb[i]s | AADRL

Thesis | 2018-2020

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Research Illumination Systems Smart Highway project : Daan Roosegaarde Glowing Lines charge during day-time and glow at night for several hours to create an iconic highway experience and increase safety. The project is part of the Smart Highway.

Glowing Lines uses photo-luminescent paint to mark out the edges of the road, and is the first of five concepts to be realized from Dutch designer Daan Roosegaarde’s Smart Highway project – designed to make highways safer while saving money and energy.

SMART HIGHWAYS are interactive and sustainable roads of tomorrow by Daan Roosegaarde and Heijmans Infrastructure. Its goal is to make smart roads by using light, energy and information that interact with the traffic situation.

“Here the landscape becomes an experience of light and information,” said Studio Roosegaarde in a statement. “As a result this increases visibility and safety.”

The lines are now installed along the N329 route in Oss for an initiative called Road of the Future. Three glowing green lines run along each side of the dual carriageway and illuminate every night.

Roosegaarde described driving along the section of road at night as “going through a fairy tale”.

Glow lines illuminating by the night on the Smart Highways

Research

Parallels : Nonotak Studio Parallels is an audio visual installation by Noemi Schipfer and Takami Nakamoto, commissioned by STRP. Like in all their installations Parallels explores light as a material, but this time the space as a whole becomes their screen. The boundaries and notion of space, become abstract as the audience crosses the room, but in doing so, the audience also affects the space by breaking the light. This installation is strongly connected to the space in which it takes place; it lives within it. But as soon as the light hits the walls that define the space it reaches its limits and stops reproducing itself.

The installation is also inspired by Anthony Mccall’s exploration of light and space.

The varied lighting effects providing a varying sense of space even within the same given space.

Orb[i]s | AADRL

Thesis | 2018-2020

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Unit Geometry History of spheres

1. Cénotaph de Newton

Designed by Étienne-Louis Boullée in 1784, this unbuilt structure is considered a purely modern or futuristic invention.

It is a reminder that they have been a source of visionary architectural inspiration and fascination for centuries. Given neoclassicism’s obsession with geometry and proportion and the Enlightenment’s preoccupation with technology and advancement, it is perhaps no surprise that some of the biggest and most ambitious spherical projects were conceived not in the 20th or 21st centuries but in the 18th.

Chief among them was the stupendous plan for a French mausoleum to Sir Isaac Newton. Although Boullee’s stone and concrete monument was never built, at 150m high it would have overtaken the Pyramids to become (at the time) the tallest building in the world.

Research

2. Montreal Biosphère

Buckminster Fuller is the visionary architect-inventor who revolutionized the design of spherical buildings with his ground-breaking geodesic dome.

Despite their name, geodesic domes are actually spheres whose surface is formed by a series of tessellated triangular elements.

Fuller used this principle to develop a perfect form of sphere that was lightweight and potentially portable.

He built several, including his own home, but his most famous is the pavilion he built in Montreal for Expo 67 – the 1967 world fair – which is now part of the Biosphère environment museum. An enclosed structure of steel and originally acrylic cells, the 76m wide and 62m high semi-submerged sphere inspired several imitators, including the famous 1982 Spaceship Earth (Epcot) sphere at the Walt Disney World Resort in Orlando, Florida.

Orb[i]s | AADRL

Thesis | 2018-2020

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Unit Geometry Advantages of a Sphere

As an object a sphere actually has the optimum surface-area-to-volume ratio: it’s very efficient.

While looking at a bubble floating in the air, it’s a perfect sphere because that shape provides it with the optimum lightweight structure to support itself and withstand the forces around it.

Imagine a raindrop hitting the earth – it’s forced to change its form and behaviour because the forces acting against it change. Dealing with those forces and maintaining the form presents a unique challenge.

How a sphere is built is as critical to its feasibility as what it is built from.

As James Law explains, new developments in modular construction could accrue efficiencies. “If a spherical building enjoys repeatable structural elements and easy connecting details, and if it can be based on a mass-produced and prefabricated set of structural components, it can be just as efficient and economical as a rectilinear form.”

Research

Mobility

Area

Packing ability

R

Optimum Surface Area to Volume Ratio

R

Perfectly Symmetrical

No defining edge

Orb[i]s | AADRL

Thesis | 2018-2020

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Unit Geometry Packing Strategy

When it comes to packing 3D objects, spheres have one of the most efficient close packing possibilities amongst others shapes. Spheres are also easily re-configurable and re-adjust to connect with the neighboring units upon any disturbance in the system. This led us to choosing to study this geometry and its possibilities in detail, since our goal is to achieve growth in all six axes.

Several other geometries tested before spheres.

Millions of black plastic balls covering the Los Angeles Reservoir, in order to protect the region’s drinking water.

Research

Spheres provided the optimal packing ability in all conditions

96 million black plastic balls covering the Los Angeles Reservoir.

Orb[i]s | AADRL

Thesis | 2018-2020

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Initial Tests

Signal Transmission Seeding Patterns |

Variable start points of growth

Behavior Based on Neighbors |

Growth based on neighbor proximity

Initial Tests

Signal Transmission

Signal Transmission

Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 1

Plan

Connection between 2 components

Initial Tests

Exploded Axonometric

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Thesis | 2018-2020

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Component Iterations Iteration 1

Top view of Connection between 2 components

Front view of Connection between 2 components

Initial Tests

Top view of Connection between 3 components

Front view of Connection between 2 components Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 1 | 2 Component transformation

1.

2.

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4.

Initial Tests

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8.

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Thesis | 2018-2020

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Component Iterations Iteration 1 | 4 Component transformation and mobility

Transformation State 1

Transformation State 2

Initial Tests

Mobility Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 2

Exploded Axonometric

Initial Tests

State Transformation

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Thesis | 2018-2020

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Component Iterations Iteration 3 | Component Connections

SINGLE UNIT

FRONT VIEW

SIDE VIEW

NGLE UNIT

FRONT VIEW

SIDE VIEW

SINGLE UNIT Flexible joint

Flexible joint FRONT VIEW

Flexible joint

Rigid legs

TWO UNITS COMBINED

SIDE VIEW

Flexible joint

Rigid legs

Flexible joint

Flexible joint

Rigid legs

WO UNITS COMBINED

Ball joint detail

Ball joint detail

TWO UNITS COMBINED Ball joint

Ball joint detail

Ball joint

Ball joint

Initial Tests

Two units Two units Two units Two units

Four units Four units Four units Four units

Consolidated form Consolidated form Consolidated form Consolidated form

Expanded form Expanded form Expanded form Expanded form

PERSPECTIVE PERSPECTIVE PERSPECTIVE PERSPECTIVE

ELEVATION ELEVATION ELEVATION ELEVATION

PLAN PLAN PLAN PLAN

Single Unit Single Unit Single Unit Single Unit

Family of 1, 2 & 4 Unit connections Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 3 | Tiling Patterns in Compressed State Two units Two units Two units

Four units Four units Four units

PERSPECTIVE PERSPECTIVE PERSPECTIVE PERSPECTIVE

ELEVATION ELEVATION ELEVATION ELEVATION

PLAN PLAN PLAN PLAN

Single Unit Single Unit Single Unit

4 Component Transformative States

Consolidated form Consolidated form Consolidated form

Expanded form Expanded form Expanded form

Initial Tests

Consolidated form Consolidated form Consolidated form

Four units Four units Four units

Expanded form Expanded form Expanded form

ELEVATION

PLAN

Two units Two units Two units

PERSPECTIVE

PERSPECTIVE PERSPECTIVE PERSPECTIVE

ELEVATION ELEVATION ELEVATION

PLAN PLAN PLAN

Single Unit Single Unit Single Unit

4 Component Transformative States Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 3 | Aggregation with Different States

Closed State 1.

2.

3.

4.

Semi-Open State

Initial Tests

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Orb[i]s | AADRL

Thesis | 2018-2020

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Component Iterations Iteration 3 | Aggregation Patterns in Semi-Open State

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Initial Tests

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Component Iterations

TILING PATTERNS OF COMPONENT IN COMPRESSED STATE

Iteration 3 | Tiling Patterns in Compressed State TILING PATTERNS OF COMPONENT IN COMPRESSED STATE

its

1 Unit

2 Units

3 Units

5 Units

6 Units

7 Units

2 Units

1 Unit

MPRESSED STATE

3 Units

3 Units

4 Units

4 Units

4 Units

5 Units

5 Units

6 Units

7 Units

7 Units

8 Units

8 Units

Initial Tests

Aggregation

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Thesis | 2018-2020

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Component Iterations

Unit test 01

Unit test 02

Unit test 03

Initial Tests

Legs for x,y,z axis

Expansion capability

Expansion capability

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Unit Development

Component Iterations Iteration 4 | Transformation of a single component

Single Unit : Transformation SINGLE UNIT : Transfomation

SINGLE UNIT : Rotations

Two Units

Connection Transformation

Connections and transfomations

TWO UNITS

Three Units

THREE UNITS

Four Units

FOUR UNITS

SINGLE UNIT : Transfomation

Unit Development

Single Unit : Rotation SINGLE UNIT : Rotations

Eight Units

MULTIPLE UNITS

Multiple Units

Connections and transfomations

EIGHT UNITS

Orb[i]s | AADRL

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Left + Down Rotation

Left + Down Rotation

Left + Down Rotation

Left + Down Rotation

Right + Right + Up Down Rotation Rotation

Right + Right + Up Down Rotation Rotation

Right + Right + Up Down Rotation Rotation

Right + Right + Up Down Rotation Rotation Left + Up Rotation

Left + Up Rotation

Left + Up Rotation

Left + Up Rotation

Rightward Rotation

Rightward Rotation

Rightward Rotation

Rightward Rotation

Leftward Rotation

Leftward Rotation

Leftward Rotation

Leftward Rotation

Downward Rotation

Downward Rotation

Downward Rotation

Downward Rotation

Upward Rotation

Upward Rotation

Upward Rotation

Upward Rotation

Component Connection Catalog

Connection between 2 Components

Upward Upward Downward Upward Downward Upward Leftward Downward Leftward Rightward Downward Leftward Rightward Left Leftward Rightward + UpLeftLeft Rightward + Up+Left Down Left + Up Right +Left Down Left ++ Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotatio Rota

Unit Development

rd ghtward ward Downward Leftward Rightward Left + Leftward Up Rightward Left Left+ +UpRightward Down Left Left Right + +Up Down +Left Left Up Right +Right +Up Down + +Up Left Down Right Right + Down + +Up Down Right Right++Up Down Right + Down on tation tion Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation Rotation

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Local Organizations Iteration 4 | 4 Component Connection & Mobility

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Unit Development

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This illustrates the unit-unit connection and the aggregation (tetrahedron pyramid) it results in.

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Orthogonal & Organic Formations Regular Angle Connection

Unit Development

Variable Angle Connection

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Component Mechanism Rotation and Expansion

30

30

Unit Development

Outer Shell Rotating Mechanism (Outer Shell)

Extendable Leg (1) Extendable Leg (2) Magnet

Rotating Mechanism (Inner)

Fully Extended Leg

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Unit States Growth in X-Y plane Two types of units are employed in forming the proposed structures Active and Passive units. Each type of unit has a specific function and position in how the structure is formed.

Active unit Passive unit

Growth with 8 components

Growth with 15 components

Growth with 22 components

Growth with 33 components

Unit Development

Growth in X-Y-Z plane The passive units are compacted with their legs retracted into the sphere which inhibits their ability to ascend vertically on the Z-axis. These units can move around, and connect to other units on the base layer but don’t have the capacity to illuminate.

The active units possess full expansion capabilities of the legs and can light up when required to do so.

Passive Unit

Active Unit with illumination

Growth with 14 components

Growth with 50 components Orb[i]s | AADRL

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Unit States

State -1 : Passive State

Expansion : 0 - no expansion Potential : Tangential Connections - Packing Ability

State 0 : Active Potential State R

Expansion of one unit: ‘R/2’ (half the radius of unit) Total Expansion : ‘R’ (radius of unit) Potential : Linear connections

State +1 : Active Executive State

Expansion of one unit: ‘R’ (radius of unit) Total Expansion : ‘D’ (Diameter of unit) Potential : Diagonal connections

Unit Development

Active State Abilities

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System Scales

Unit

Unit-Unit

Unit Development

Unit-Human

Unit-System

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2

System Aggregation

Deformation in a System Deformation in a 2D Mesh Case 1 | One Signal initiated on the periphery of the grid

System Aggregation

Case 2 | One Signal initiated inside the grid

Case 3 | One Signal initiated inside the grid

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Deformation in a System

Case 4 | One Signal initiated inside the grid

Case 5 | Two Signals initiated in the grid

System Aggregation

Deformation in a 3D Mesh Case 1 | One Signal initiated inside the System

Case 2 | One Signal initiated on the periphery of the System

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Component Aggregation Growth Triangular Aggregation

1 Component

Linear Growth with 10 components

Planar Growth with 10 components

Planar Growth with 200 components

Planar Growth with 50 components

System Aggregation

Tetrahedron Aggregation

1 Component

Linear Growth with 10 components

Planar Growth with 10 components

Planar Growth with 50 components

Planar Growth with 200 components Orb[i]s | AADRL

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Component Aggregation Growth Natural Triangular Aggregation

Growth with 10 components

Growth with 50 components

Growth with 200 components

Growth with 1000 components

System Aggregation

Natural Tetrahedron Aggregation

Growth with 10 components

Growth with 50 components

Growth with 200 components

Growth with 200 components Orb[i]s | AADRL

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Component Aggregation Growth Signal Transmission

This catalog is to study the trajectory of an activation signal as it passes through different aggregation growth patterns.

System Aggregation

Organised & Natural Aggregations

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Component Aggregation Growth

In order to achieve a larger System of detection of neighbours, their connections and reconfiguration to further achieve the most efficient configuration based on imbibed rule sets, an NK network could be developed as follows:

K=0 and N=5

K=1 : the better state of each node depends on the state of the node that depends on it and the state of the node it depends on.

System Aggregation

K increases : the better state of each node depends on the state of the nodes that receives its inputs and the states of the nodes that provide inputs. In this case N=5, K=2.

The wheel is more like an organisation where all the information is filtered through a central unit.

The circle topology indicates the interaction of units with their ‘k’ nearest neighbours

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Units Connection Possibilities Different connection options with a varying number of units is explored here.

-1 1.

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State

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-1 State 7.

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System Aggregation

-1 4.

State

1

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-1

State

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16.

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Unit Connection States

-1

State

1

-1

State

1

-1

State

1

-1

State

1

-1

State

1

2 units

3 units

3 units

4 units

4 units

System Aggregation

-1

State

1

-1

State

1

-1

State

1

-1

State

1

4 units

5 units

5 units

6 units

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Transformation in a Triangle Pattern In these tests of expansion in a triangle pattern system, we studied expansion as a line and as a surface to study what transformation could be achieved.

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System Aggregation

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Transformation in a Pyramid Pattern In these tests, some parts of the system expand and some of them contract. We tested this to understand the transformative behaviour of the system.

System Aggregation

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Transformation in a Square Pattern In these tests of expansion in a cubical system, we studied expansion as a line, as a surface and as a volume to study what transformation could be achieved.

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System Aggregation

Expansion and Contraction in a Square Grid 256 units

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Transformation in a Square Pattern In a cubical system with a square connection pattern, transformation was studied by disconnecting some of the units and anchoring the bottom units. Once units went from State -1 to State 1, the transformation would vary based on the position of units that disconnect.

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3.

1.

2.

3.

1.

2.

3.

System Aggregation

Expansion and Contraction in a Square Grid 1386 units

1.

2.

3.

4.

5.

6.

1.

2.

3.

4.

5.

6.

Orb[i]s | AADRL

Thesis | 2018-2020

108

Transformation in a Square Pattern In these tests, some parts of the cuboidal system expand and some of them contract. We tested this to understand the transformative behaviour of the system and how it could give us usable spaces.

1.

2.

3.

1.

2.

3.

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3.

1.

2.

3.

System Aggregation

1.

2.

3.

1.

2.

3.

1.

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3.

1.

2.

3.

Orb[i]s | AADRL

Thesis | 2018-2020

110

Transformation in a Square Pattern The units expand and contract based on linear signals generated within the system.

Square Pattern State -1

Triangle Pattern State -1

Pyramid Pattern State -1

State 0

State 1

State 0

State 1

State 0

State 1

System Aggregation

Structural Analysis Cantilevering Unit

2x2x2 grid

6 Stable Units 1 Cantilevered Unit

2x2x3 grid

8 Stable Units 2 Cantilevered Unit

2x2x2 grid

12 Stable Units 2 Cantilevered Unit

6x6x5 Grid | 145 units

26 Units

34 Units

31 Units

30 Units

24 Units

Orb[i]s | AADRL

Thesis | 2018-2020

112

Arch Formation In these tests, by studying the different connection patterns, we studied if the transformation could create an arch. This was only successful in a pyramid system.

Square Pattern State -1

Triangle Pattern State -1

Pyramid Pattern State -1

State 0

State 1

State 0

State 1

State 0

State 1

114

System Aggregation

Arch Formation in a Pyramidal Structure 200 units

0.96m

55% Expansion

60% Expansion

State 0

1.44m

70% Expansion

65% Expansion

1.8m

1.6m

75% Expansion

2.2m

85% Expansion

1.2m

80% Expansion

2.4m

90% Expansion

2.6m

95% Expansion

2.8m

100% Expansion State 1

Orb[i]s | AADRL

Thesis | 2018-2020

Grid Formation The units sense and connect in a gridded manner along the axes of the legs. Once the first layer is completely filled, the units start climbing over to the next layer. Next, the units start optimizing space occupation and try to occupy maximum number of available spaces in the grid

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

System Aggregation

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

Orb[i]s | AADRL

Thesis | 2018-2020

116

Triangle and Pyramid Formation The units connect to form a triangle in one layer with the legs fully extended. Then with the aid of a helper unit,another unit climbs to the next layer. The unit then rotates and extends it legs to connect and form a pyramid.

1.

2.

3.

4.

5.

6.

7.

8.

9.

Triangle Formation

System Aggregation

10.

11.

12.

13.

14.

15.

16.

17.

18.

Pyramid Formation Orb[i]s | AADRL

Thesis | 2018-2020

118

2

Structure Formation

Structure Formation The Units assemble and disassemble to create different typologies of spaces dependent on user needs and patterns.

1.

2.

3.

4.

5.

6.

Structure Formation

1.

2.

3.

4.

5.

6.

7.

Orb[i]s | AADRL

Thesis | 2018-2020

128

Structure Formation

1.

2.

3.

4.

5.

6.

7.

Structure Formation

1.

2.

3.

4.

5.

6.

7.

Orb[i]s | AADRL

Thesis | 2018-2020

130

Structure Formation This illustrates the transformative nature of the pyramidal system by virtue of connection and contraction of the legs.

32 units

1.

2.

3.

4.

5.

6.

7.

8.

Structure Formation

Temporary Scaffolds in Structure

Studies into the creation of a temporary structural elements which could aid in the formation of a structure, was also studied.

805 units

1.

2.

3.

4.

5.

6.

7.

8.

Orb[i]s | AADRL

Thesis | 2018-2020

132

Structure Formation Gradient Adaptation Following Gradient

1. Target destination on a gradient

2. Units follow the gradient

3. Units adapt and connect

4.Formation along gradient

Not Following Gradient

1.Units come together

2. Units climb over one another

3. Units maintain level, not following gradient

4. The collective of the units maintain one level.

Structure Formation

Following Gradient

1.Units add on to an existing aggregation

2. Level varies while climbing occurs

3. Levels vary in the process of climbing

4.Original level restored

Gradient Adaptation

5.

Orb[i]s | AADRL

Thesis | 2018-2020

134

Goal Oriented Behaviour Different goals of aggregating linearly, forming seating, vertical aggregation and forming a stepped structure are explored here.

1.

2.

3.

4.

5.

6.

7.

8.

Structure Formation

Linear Aggregation

Stepped Structure

Vertical Aggregation

Seating Formation Orb[i]s | AADRL

Thesis | 2018-2020

136

Structural Typologies

Number of Units: 40 State : -1

Number of Units: 40 State : 1

Number of Units: 40

Structure Formation

Number of Units: 55

Number of Units: 55

Number of Units: 55 Orb[i]s | AADRL

Thesis | 2018-2020

138

Structural Typologies

Number of Units: 90

Number of Units: 60

Number of Units: 54

Structure Formation

Number of Units: 110

Number of Units: 160

Number of Units: 275 Orb[i]s | AADRL

Thesis | 2018-2020

140

Behaviour

Goal-Oriented Behaviour Singular Target

Active Unit

Target

1.

2.

3.

4.

5.

6.

The active unit emitting the signal moves towards the target location.

The other units follow the active unit to ultimately reach the target.

Behaviour

5 Different Targets

Targets

1.

Units

5.

2.

6.

3.

7.

4.

8.

The units aggregate towards the corresponding target points of the same color to form the desired structure.

Orb[i]s | AADRL

Thesis | 2018-2020

150

Goal-Oriented Behaviour Multiple Obstacle Avoidance

Target

1.

Obstacles Units

5.

2.

6.

3.

7.

4.

8.

The units learn to avoid and navigate through obstacles to reach the target location.

Behaviour

5 Different Targets & Obstacle Avoidance

Target

Obstacles

Units

1.

6.

2.

7.

3.

8.

4.

9.

5.

10.

The units aggregate towards the corresponding target points of the same color to form the desired structure while avoiding obstacles.

Orb[i]s | AADRL

Thesis | 2018-2020

152

Goal-Oriented Behaviour Overcoming uneven terrain and structure formation

Target

1.

2.

Units

Obstacle

4.

5.

3.

6.

The units learn to manouver over uneven terrain to reach the target location and build the desired structure.

Behaviour

Orb[i]s | AADRL

Thesis | 2018-2020

Sensing & Communication Signal Detection | 1 Signal

1. Signal Initiated in the Active unit

Proximity Detection

2. Other units detect the signal

Direction Navigation

3. Units seek and aggregate towards the signal

Behaviour

Obstacle Avoidance

Target

1.

Unit

Target activated

Obstacle & Neighbor Avoidance

Neighbor Avoidance

1.

Behavior

Target

Unit

Unit Obstacle

2.

Unit Avoids Obstacle

2.

Unit start moving towards Target

3.

Unit moves towards Target

3.

More units moves towards Target

4.

Unit moves towards Target

4.

Units move towards Target

5.

Unit reaches Target

5.

All units reach Target

Orb[i]s | AADRL

Thesis | 2018-2020

156

Sensing & Communication Signal Detection | 3 Signals

Signal 1

Signal 2

Signal 1 & 2 activated

Signal 3

Signal 3 activated and units move towards Signal 1 & 2

Behaviour

Units start to seek and aggregate towards signals 1 & 2 & 3

Units aggregate towards the signals Orb[i]s | AADRL

Thesis | 2018-2020

158

Communication Transfer of information

Within a self-structuring and self-organizing system, the transfer of information is key. A constant loop of to and fro information is essential in order for the system to respond promptly to the changes within and outside the system.

Stimulus

Message

Receptor

Response

Message

Control Apparatus

Stimulus

Feedback Loop In our system, information loops are set up in at two levels: - Local Level At this level responses would be stimulated within the system at a more regional level, evoking responses from individual units These stimuli are generally territorial and are evoked based on their neighbouring units or immediate context.

- Global Level At the global level stimulus is provided to the system at large.

Two kinds of stimuli are provided to the system one based on context and environmental conditions and one through a cloud based server.

The former is still a local feedback loop within the system, its the collective response of the unit that makes the response seem like a global response.

Whereas, the latter information loop will be used to aid the location of the units in helping tackle dinginess, inactivity and induce a sense of safety. Movement and illumination would be provided in areas that are marked unsafe, promoting safer access and a psychological comfort to pedestrians accessing these spaces.

Behaviour

Locally

Environmental changes Movement patterns

Passive

Neighbouring units

Stimulus

Active

Feedback Loop

Globally

Orb[i]s | AADRL

Thesis | 2018-2020

160

Sensing & Communication Multiple Targets & Obstacle Avoidance Static Obstacles

Dynamic Obstacles

Inactive Units Active Units

Units and Obstacles in a given field

3 Targets activated Artificial Intelligence is used to stimulate the inactive units at random trajectories to reach the closest target, resulting in a different outcome each time. AI also enables a real-time avoidance of Static and Dynamic obstacles.

Behaviour

Units learn to avoid Static and Dynamic Obstacles and try to reach Targets

Units reach their Targets

Orb[i]s | AADRL

Thesis | 2018-2020

162

Sensing & Communication Range Finding & Obstacle Avoidance Static Obstacles

Dynamic Obstacles

Inactive Units Active Units

Target, Units and Obstacles in a given field

Units get activated when in range of Target The target unit is programmed with the capacity of range finding and a fixed angle for its radar detection.

Behaviour

In Range < 20 Out of Range > 20

Other Units get activated (grey -> orange) when in range of Target (distance < 20)

More Units within the range of the Target

Orb[i]s | AADRL

Thesis | 2018-2020

164

Sensing & Communication Signal Transmission & Obstacle Avoidance Static Obstacles

Dynamic Obstacles

Inactive Units Active Units

1. Passive and active agents, with obstacles in a given environment.

2. Passive agents get activated when in range of active agents. Agents learn to avoid obstacles with their angle detection capability.

Behaviour

In Range < 20 Out of Range > 20

Angle of Detection : 35°

3. Upon activation through range finding, agents started to display a clustering behavior with fellow active agents.

Total time taken : 7 minutes 35 seconds

4. The total time taken for the activation of all agents in a given environment varies from agent speed and range of detection.

Orb[i]s | AADRL

Thesis | 2018-2020

166

Physical Sensing Prototype Inner Rolling Mechanism Exploded Axonometric

Outer Shield

Motor Shield

[2 x L293D Motor Driver]

Arduino Uno R3

Soft Silicon Wheel [38 x 11mm] 6V Batteries Micro DC Motor

[Gear Box | Speed Reducing] Gear Ratio Free-Run Speed Free-Run Current Motor Type Size Weight Shaft Diameter

50:1 30 rpm 120 mA 1.6 A Stall @6V (HP 6V) 35 x 12 x 15 mm 9.5 g 3 mm

Goal-Oriented Behaviour Obstacle Avoidance

Blue: No obstacle detected

Red: Obstacle detected

Behaviour

Light Sensing

Red: Unit Inactive

Blue: Unit Activated

Orb[i]s | AADRL

Thesis | 2018-2020

170

Goal-Oriented Behaviour Singe Target

Active Unit

Target

1. Target detected

Active Unit

Target

2. Move towards target

Active Unit Target

3. Target achieved

Behaviour

Colour Based Behaviour

Target

Active Unit

[In Range]

Target

Target

Target Detected

Target Achieved

Orb[i]s | AADRL

Thesis | 2018-2020

172

Collective Intelligence

Machine Learned Systems Initial Research What is Machine Learning?

Machine Learning is an application of artificial intelligence that provides a system with the ability of learning from data autonomously, rather than in an engineered way.

This works by feeding the system with information and observations that can be used to find patterns and make predictions about future outcomes.

More broadly, the system has to learn the desired input-output mapping.

This way, the system will be able to choose the best action to perform next in order to optimize its outcome. There are different ways of doing this depending on what kind of observations we have available to feed our system.

The one used in this context is Reinforcement Learning. Reinforcement Learning is a learning method based on not telling the system what to do, but only what is right and what is wrong. This means that we let the system perform random actions and we provide a reward or a punishment when we think that these actions are respectively right or wrong. Eventually, the system will understand that it has to avoid making mistakes and earn maximum rewards.

We most commonly opt for Reinforcement Learning to train a Neural Network, which is a computer system modeled on the nervous system.

Neural Networks: a Computerâ&#x20AC;&#x2122;s Nervous System

Collective Intelligence

In Unity, our â&#x20AC;&#x153;agentsâ&#x20AC;? (the entities who perform the learning) use the Reinforcement Learning model. The agents perform actions in a given environment.

The actions cause a change in the environment, which in turn is feed back to the agent, together with some reward or punishment. The action happens in what we call the Learning Environment.

State & Rewards

State Transition

Environment

Agent

Action

Machine Learning is changing the way we expect to get intelligent behaviour out of autonomous agents. Whereas in the past the behaviour was coded by hand, it is increasingly taught to the agent (either a robot or virtual avatar) through interaction in a training environment. This method is used to learn behaviour for everything from industrial robots, drones, and autonomous vehicles, to game characters and opponents. The quality of this training environment is critical to the kinds of behaviours that can be learned, and there are often trade-offs of one kind or another that need to be made.

Machine Learning becomes integral in our system to be able to achieve efficient autonomy. The unique and non-predefined behaviour in the system would arise from this kind of decision making. The agents in a given environment will learn from its experience and ensure to perform a more efficient action each time, making the system smart, behaviour driven and autonomous.

Orb[i]s | AADRL

Thesis | 2018-2020

178

Q-Learning

Within the scope of Reinforcement Learning, Q-Learning provides us a function which helps estimate long term values of taking certain actions in certain circumstances.

The goal with Reinforcement Learning is to train an agent which can learn to act in ways that maximizes future rewards within a given environment.

Q-Learning finds the optimal path to the goal when the path is unknown/ cannot be predicted.

The primary difference in Q-Learning and Reinforcement Learning is that of the ‘contextual bandit’ working in Q-learning i.e., there is a lack of sparse rewards and state transition when compared to the latter. The agent doesn’t receive a reward for each action taken, rather waits till an optimal number of actions have been taken.

This estimate value improves with time, optimizing the actions taken.

Testing the Agents in a Grid World of Q-Learning An environment with: • • • •

Agents : randomly placed Goals : randomly placed - position we wish for the agents to learn of Obstacles : randomly placed - which we want our agents to avoid States : in the environment that will correspond to the grid.

At the inception of the training the Q-value estimate is poor. The Q-value corresponds to the true Q-function of the environment, therefore, actions taken tend to be increasingly accurate.

The point based system of learning aids the reward-punishment learning system.

Points: • +1 : for moving towards the goal • - 1 : for moving towards an obstacle • - 0.05 : for each move made (encouraging quicker movement).

Collective Intelligence

Orb[i]s | AADRL

Thesis | 2018-2020

180

Collective Intelligence Behaviour Designer

In order to study Collective Intelligence in our system with efficient Machine Learning, behavioural studies became integral.

Behaviour of agents lay the basis of Collective Artificial Intelligence.

Thus, by using Behaviour trees in our simulations, we were able to achieve unpredicted AI. Each agent within a system behaves uniquely, having different goals and different ways of achieving them. But what happens when 100’s of these agents come together?

Each agent’s behaviour contributes to a collective whole behaviour.

In order to understand this collective behaviour better, we did several tests with a varied set of behaviours.

One of our initial tests was the ‘Seeker-Attractor’ Behaviour, where one agent would have the property to seek and another to attract the seeker.

By merely varying the proportions of Seeker to Attractors within a given environment, we observed a large variation in clustering patterns and formations.

Rules : Attractor

Seeker

• Attractors freely navigate a given space, taking random paths each time - much like analyzing the environment.

• Attractors repel each other when within a distance ‘X’.

• Seekers remain stationery unless they come within range ‘Y’ of an Attractor.

• Seekers keep count of the number of collisions with an attractor, using which an agent determines which attractor it can establish a stronger connection with.

Collective Intelligence

1 Attractor 5 Seekers

2 Attractors 10 Seekers

3 Attractors 30 Seekers

4 Attractors 40 Seekers

5 Attractors 50 Seekers

6 Attractors 60 Seekers

Common Cluster formations observed

Interaction between attractors and seekers

Orb[i]s | AADRL

Thesis | 2018-2020

182

Collective Intelligence Detecting Neighbouring Agents

Establishing a Relationship In order to develop collective behaviour, agents must have the ability to sense and detect fellow agents in a given context.

An apt angle of detection as well as range of detection is key for identification of neighbours followed by establishment of suitable connections.

By providing agents with a 360 degree sensing ability, the connections with other agents are then not limited to a particular direction.

To understand this relationship better, we treated our agents as particles. Parameters like Velocity, Gravity and Particle Distances were predetermined to get optimal results.

By varying the number of agents in the same given space, a large number of patterns in raycasts were observed, much like a large interconnected network.

In this case the Optimal values for interaction were fixed at : â&#x20AC;˘ Range : 0 - 85 units â&#x20AC;˘ Agent Velocity : Velocity_Y direction + 0.8

5 Agents

10 Agents

20 Agents

50 Agents

100 Agents

200 Agents

500 Agents

1000 Agents

Collective Intelligence

Flocking / Swarming Behaviour Flocking Behaviour was introduced in the system by applying and varying the following forces. A different and unpredicted pattern was observed with variation in these forces as well as with number of agents present.

Alignment

Cohesion

Alignment enables the movement of agents in a common direction. Each heading will set towards an average heading direction.

Cohesion compels each agent in a flock to stay grouped with its neighbours.

Agent finds a mid point between all its neighbours and then navigates towards it.

Separation

Separation helps prevent collisions and overlaps of agents. If other agents get too close in radius, the agent will navigate away from these neighbours.

5 Agents

10 Agents

20 Agents

50 Agents

100 Agents

200 Agents

500 Agents

1000 Agents

Alignment : 0.8 Cohesion : 0.15 Separation: 0.25

Orb[i]s | AADRL

Thesis | 2018-2020

184

Collective Intelligence Units aggregating in a single layer

Collective Intelligence

Units aggregating to grow vertically

Orb[i]s | AADRL

Thesis | 2018-2020

186

Stigmergy In higher population studies, these units depict intelligent behaviors in flocking strategies. They begin to display stigmergic properties wherein the agents sense and follow the other units closer to the target. Constrained by the axial mobility properties of the unit.

1750 Units | 1 Flock

Collective Intelligence

Target

Orb[i]s | AADRL

Thesis | 2018-2020

188

Stigmergy These flocks also collectively move as per the displacement, while being constrained by the axial mobility properties of the unit.

3000 Units | 2 Flocks

Collective Intelligence

Target

Orb[i]s | AADRL

Thesis | 2018-2020

190

Crowd Segregation Depending on the number of units present in a given location, they divide the space differently resulting in spaces of different user occupancies.

50 Units

200 Units 10-40 Person Occupancy

40-100 Person Occupancy

Collective Intelligence

2000 Units

2000 Units Movement Patterns

Orb[i]s | AADRL

Thesis | 2018-2020

192

Path Formation Upon contact with a human agent, the units self-organize to move away and clear the path taken by the human.

2000 Units

Collective Intelligence

Orb[i]s | AADRL

Thesis | 2018-2020

194

Prototyping

Prototype Detail Prototype Components

Exoskeleton

Extendable Arm Mechanism

Connection Point

Inner Rolling Mechanism

Prototyping

125

Outer Surface Geometry Initial Exoskeleton Prototypes Pros: Symmetrical Light weight

Cons: Less Curvature Inability to roll

Pros: Increased Curvature Light weight

Cons: Asymmetrical Inability to roll

Pros: Increased Curvature

Cons: Asymmetrical leading to undistributed weight Inability to roll

Prototyping

Pros: Increased Curvature

Cons: Asymmetrical Heavy Difficulty Rolling

Pros: Increased Curvature Light-weight Symmetrical

Cons: Difficulty Rolling smoothly

Pros: Increased Curvature Very Light-weight Symmetrical

Cons: Difficulty Rolling smoothly

Orb[i]s | AADRL

Thesis | 2018-2020

202

Prototype Exoskeleton Detail Mobility Test

Subdivisions Level: 1

Ease Of Rolling

Subdivisions Level: 2

Subdivisions Level: 3

Subdivisions Level: 4

Prototyping

Kerfing Pattern on Rubber Track

Rigid Frame Indent on geometry for a flushed track

Geodesic Triangular Geometry

Orb[i]s | AADRL

Thesis | 2018-2020

206

Prototype Exoskeleton Detail

Force Transfer Compression Ring

Addition of compression rings to stabilize nodes

Increasing Triangular Subdivisions

Increasing Triangular Subdivisions

Prototyping

Ax8

Ex8

Bx4

Fx8

Cx8

Gx4

Dx8

Hx8

Orb[i]s | AADRL

Thesis | 2018-2020

210

Materiality Recycling - Upcycling Itâ&#x20AC;&#x2122;s a known fact that plastics are hard on the environment. While several organizations and establishments have recycling programs, the amount of plastic produced each year overshadows the amount that gets recycled. Significant amount of energy goes into the collection and processes of recycling, which nullifies its purpose.

Moving past the entire process of collection and commuting, 3D printing has disrupted these barriers. With its ability to decentralize manufacturing, parts can be emailed and printed on location.

Some companies, such as b-pet, ProCycler and Filabot are making desktop solutions to grind and extrude your own polymer practically anywhere.

Recently, researchers at Michigan Technological University in Houghton Mich., released all the open-source files online to build a machine that can recycle polymers and turn them into filament.

Plastic Waste

ProtoCycler : Plastic

3D Printing : Prototype

One method of distributed plastic recycling is to upcycle plastic waste into 3D printing filament with a recyclebot, which is an open-source waste plastic extruder. Previous research on the life cycle analysis (LCA) or the recyclebot process using post-consumer plastics instead of raw materials, showed a 90% decrease in the embodied energy of the filament from the mining, processing of natural resources, and synthesizing compared to traditional manufacturing.

Prototyping

In addition, the recyclebot provides the potential for consumers to recycle plastic in their own homes to save money by offsetting purchased filament.

Recyclebots are also useful for laboratory and industry prototyping research, as failed prototypes are recycled into filament for future work.

There have been many versions of recyclebot developed by both companies (e.g., Filastruder) as well as individuals (e.g., Lyman), including open-source versions from the Plastic Bank, Precious Plastic, and Perpetual Plastic. There are also several commercial versions of the recyclebot, including the Filastruder, Filafab, Noztek, Filabot, EWE, Extrusionbot, Filamaker (which also has a shredder), Strooder, and Felfil (OS), that can work with waste plastic.

Orb[i]s Out in the real world

Wear and Tear

Orb[i]s Out in the real world

Orb[i]s | AADRL

Thesis | 2018-2020

212

Recharging Potential Our system is incorporated with the ability to recharge itself with wireless inductive charging using the existing infrastructure in London for this purpose. For this purpose we studied the availability of these charging stations around London and found them to be in a convenient 1 mile radius.

Conserving Energy

The kinetic energy generated from braking is converted to thermal energy and reused to power the unit.

Prototyping

Inductive Recharging Mechanism

Charging up in Different Seasons

Battery charge lasts longer in summer months due to warmer ambient temperature

Summer

Winter

Orb[i]s | AADRL

Thesis | 2018-2020

214

2

Prototype Detail Inner Rolling Mechanism Exploded Axonometric

Prototyping

Wheels DC Motors Counterweight

Orb[i]s | AADRL

Thesis | 2018-2020

218

Prototype Detail Extendable Arm Mechanism Exploded Axonometric

Electric Actuator

Actuator Holder [3D Printed]

Inner Arm Tube

[6 cm Diameter | Acrylic]

Inner Arm Cap [3D Printed]

Prototyping

Electromagnet Spring

[3W | 0.13A]

Electromagnet Holder [3D Printed]

Flexible Arm Tube [4 cm Diameter]

Orb[i]s | AADRL

Thesis | 2018-2020

222

Prototype Detail Extendable Arm Mechanism Exploded Axonometric

Inner Arm Tube

[6 cm Diameter | Acrylic]

Motor Holder [3D Printed]

Micro DC Motor

[Gear Box | 12cm Shaft] Gear Ratio Free-Run Speed Free-Run Current Motor Type Size Weight Shaft Diameter Output Shaft Length

Inner Arm Cap [3D Printed]

50:1 400 rpm 170 mA 1.6 A Stall @12V (HP 12V) 144 x 12 x 10 mm 20 g 4 mm 120 mm

Prototyping

Ring Holder [3D Printed]

Outer Arm Tube

[4cm Diameter | Acrylic]

Electromagnet [3W | 0.13A]

Load Capacity Current Magnet Type Size Weight Mounting Hole

2.5 Kg 130 mA EM200815M3-5(HP 12v) 15 x 20 mm 150 g 3 mm

Electromagnet Holder [3D Printed]

Orb[i]s | AADRL

Thesis | 2018-2020

224

Detail Drawing Exploded Axonometric | Design 2

2

Unit to Uni

it connection

Flexible Extending Tube

Illumination Strategies

System Intelligence Detecting and Recording Motion Patterns

By using Passive Infrared (PIR) sensors, motion of users can be recorded within the system.

Furthermore, by recording the start and end time of the motion the duration of motion can be estimated. With this information, the system would have the ability to study and analyze user patterns and bring about meaningful transformations in the system, making the spaces further conducive.

Code used to record data using PIR Sensor : void loop() { val = digitalRead(inputPin); if (val == HIGH) { // checking if the input is HIGH digitalWrite(ledPin, HIGH); // turns LED ON if (pirState == LOW) { Serial.println(“Motion detected!”); pirState = HIGH; } } else { digitalWrite(ledPin, LOW); // turns the LED OFF if (pirState == HIGH){

}

}

Serial.println(“Motion ended!”); pirState = LOW;

Motion detected and recorded

Motion detected using PIR sensor

Illumination Strategies

With the use of Playmaker’s components, the PIR sensors capacity to record motion could be recreated and studied further in our simulations.

Using the ‘ Counter’ feature on each unit of the system, the change in data could be recorded to bring about required reconfigurations.

PlayMaker Script for ‘Counter’

Counter Initiation with motion of passers-by

Counter Incremented with further motion of passers-by

Orb[i]s | AADRL

Thesis | 2018-2020

244

System Intelligence Detecting and Recording Motion Patterns

Movement Curves

1 Person

3 People

7 People

Illumination Strategies

By adopting the Counter feature in simulations, movement patterns could be recorded. These Movement Curves could be used to trigger transformations within a formed space.

Real-Time Movement Pattern Analysis

Coinciding Patterns

Coinciding Patterns Orb[i]s | AADRL

Thesis | 2018-2020

246

09.01.2020

Bibliography

Website References • "Brume". 2012. Studio Joanie Lemercier. https://joanielemercier. com/brume/.

• "Light Pollination — High Lumen". 2020. High Lumen. https://www. highlumen.me/blog/2018/01/13/light-pollination.

• "Mapping London". 2020. Mappinglondon.Co.Uk. https://mappinglondon. co.uk/.

• Westland, Stephen. 2017. "Does Colour Really Affect Our Mind And Body? A Professor Of Colour Science Explains". Blog. The Conversation. https://theconversation.com/does-colour-really-affect-ourmind-and-body-a-professor-of-colour-science-explains-84382.

• "The Psychological Impact Of Light And Color - TCP Lighting". 2015.

TCP Lighting. https://www.tcpi.com/psychological-impact-light-color/.

• Herzog, Katie. 2013. "Why Shade Balls Aren’T Such A Great Idea After All". Grist. https://grist.org/article/why-shade-balls-arentsuch-a-great-idea-after-all/.

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Book References • Alpaydin, Ethem. 2016. Machine Learning. Cambridge, MA : MIT Press, [2016].

• Brown, Henry T. n.d. 507 Mechanical Movements - Mechanisms And Devices. New York: Dover Publications, 2005.

• Downing, Keith L. 2015. Intelligence Emerging. 12th ed. Cambridge, Massachusetts: The MIT Press, [2015].

• Engelbrecht, Andries P. 2007. Fundamentals Of Computational Swarm Intelligence. Chichester: John Wiley.

• Gehl, Jan, and Jo Koch. 2011. Life Between Buildings. London: Island Press • Hall, Edward T. 1990. The Hidden Dimension. New York: Anchor Books.

• Hensel, Michael. 2004. Emergence. Chichester: Wiley-Academy.

• Holland, John H. 2010. Emergence. Oxford: Oxford Univ. Press.

• Ijeh, I. (2018). Spherical objects!. [online] Building. Available at: https://www.building.co.uk/buildings/spherical-objects/5092601. article [Accessed 24 Dec. 2019].

• Johnson, Steven. 2001. Emergence. London: Allen Lane The Penguin Press.

• Kennedy, James, Russell C Eberhart, and Yuhui Shi. 2009. Swarm Intelligence. San Francisco: Kaufmann.

• Lanham, Michael. n.d. Learn Unity ML-Agents. Birmingham: Packt Publishing, 2018.

• Malone, Thomas W. 2015. Handbook Of Collective Intelligence. Cambridge, MA: The MIT Press.

• Oxer, Jonathan. 2010. Practical Arduino. [Berkeley, CA]: Apress.

Orb[i]s | AADRL

Thesis | 2018-2020