To our families and friends who helped us and supported us through this fantastic year. Through their devotion and belief, we were able to have the confidence to push through and complete this wonderful work.To our tutor Theo Spyropoulos who had the vision and introduced us to this beautiful world. To our professors especially Nick Roberts may he rest in peace, Ingalill Wahlroos-Ritter, Blagovest Valkov and Jason King. We are forever grateful for your help and critical thinking. You helped us develope our idea's and bring our project to life.
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
Design and Research Book Theodore Spyropoulos Studio
Theodore Spyropoulos Studio
Cosku Cinkilic Ahmed Shokair Pavlina Vardoulaki Houzhe Xu
Assisted by: Mustafa El Sayed Apostolis Despotidis
Team: Cosku Cinkilic Ahmed Shokir Pavlina Vardoulaki Houzhe Xu
Hyper Cell
AADRL Studio Theodore Spyropoulos Agenda: Behaviour complexity
contents: Team Members: Ahmed Shokir Cosku cinkilic Pavlina Vardoulaki Houzhe Xu
p. 15
preface
p. 73
fabrication behaviour
p. 49
system overview
p. 87
mobility
p. 57
magnetic patterns
p. 111
cell behaviour
p. 157
aggregation
p. 178
self-structuring
p. 196
settling behaviour
p. 216
thesis
Thank you: Oliviu Lugojan-Ghenciu AKT DPL Crew Li Chen Konstantin Doganov Alejandro Garcia Gadea Tvetelina Georgieva Octavian Gheorghiu Jitesh Jadhav Ali K Assad Khan Georgi Kunchev Emilia Maneva Ruxandra Matei Angel Fernando Lara Moreira Quiddale O'Sullivan Necdet Yagiz Ozkan Stafanos Panagopoulos Rui Qu Martina Rosati Kai-Jui Tsao Nhan Vo
p. 250
communication
p. 278
self-structuring
p. 320
space making
p. 344
animation
pr eface
The contemporary city today is dynamic and responsive. Architecture and urban cities have changed dramatically over the past few decades. Buildings are expected to slot into an urban grid. Ideas of urban planning as means of controlling the growth of cities based on the prediction of future development is increasingly ineffective simply because future events cannot in a present volatile dynamic society be accurately predictable. Also, the notion that architecture is observed as a single building with a single function is no longer adequate to address the
IMage
needs of society adapting to a highly mobile and ever-changing urban community. The city of the future will be able to predict the change it needs accurately and respond without top-down command. To address issues of the contemporary metropolis we were interested in developing a system that can respond to changes through self-awareness, selfassembly, mobility, and reconfigurability and create its ecology.
self-awareness self-learning self-adapting Interaction case studies
self-assembly case studies Initial experimentation
research papers collapsing emergence architecture In time, duration & space
In our exploration of self-assembly and self-awareness, we were able to identify specific features that are both interesting and allowed us to push our research further. The magnetic investigation started as a reaction to the finding we explored in self-assembly, and the behavior research reflected all of them together. Based on that, we came up with our approach to an architectural system with the ability to self-assemble and to be self-aware. From the ideas of self-learning in robotics, self-adapting, and interaction we understood how these ideas are being implemented in the robotics and artificial intelligence field. And from studying self-assembly cases studies such as Hod Lipson work, we were able to understand the required parameters we need to focus on. We primarily focused on exploring material behaviors to find an ideal combination of flexibility and mobility of a prototype in our behavior research. On the other hand, researches on magnetic patterns helped to introduce the potential of self-awareness into the system.
self- awar eness Self-awareness involves self-learning, self-adaptation, human-machine interaction and interaction which is done by oneself without assistance - referring to the current state of being adapted and to the dynamic evolutionary process that leads to the adaptation. The following topics allowed us to understand the different aspects and critical principles it takes to develop an architecture system that can respond to change over time using these elements.
s e lf -le a r n i n g Machine Learning
learning element
Machine self-learning is a subfield of artificial intelligence and plays a vital role regarding machine self-awareness. It is classified into two types as supervised learning and unsupervised learning. Supervised learning is the process in which machine is trained to infer a classification function from manually labeled training data and later to use this feature to classify new examples correctly. Different from supervised learning, no labeled training data is available for the task of unsupervised learning. Machine need to find the hidden structure in unlabeled data by clustering them based on data mining methods.
is the one responsible for modifying agent’s behavior on each iteration
curiosity element is the one that alters the behavior predicted by the learning element to prevent the agent form developing bad habits or biases
with probability P, the Curiosity element will inform to switch to a sub-optimal strategy
Reinforcement Learning Reinforcement learning differs from typical supervised learning in that no labeled examples are presented. Instead, the performance of the system at current iteration is analyzed and evaluated to bias further decisions. In a long-term reinforcement learning process, the balance between exploration and exploitation is of great importance, as exploration mechanism enables the learning agent to search in a broader landscape while exploitation mechanism is to guarantee the system evolving towards the optimal strategy.
performance element is the one that decides the action based on output of the curiosity element
keep a separate record of the outcome after playing this move
performance analyzer is the one responsible for analyzing the outcome of the decision made in the prebiouse interadion
decision
Neuro Evolution
feeding the analysis back to the Curiosity Element in the current iteration
Self-Learning Algorithms Reinforced Learning
feeding the analysis back to the Learning Element in the current iteration
Neuro Evolution
Multi-objective Neuroevolution of NPCs
Neuro evolution is a form of machine learning that uses evolutionary algorithms to train artificial neural network. It mainly applied to artificial life, computer games, and evolutionary robotics. Neuro evolution work by defining more than one fitness function, in the popular evolutionary algorithm it operates on a population of genotypes. In Neuroevolution a genotype is mapped to a neural network phenotype that is the evaluated on some task to decide its fitness. More directly what you ask of the program to achieve is more than one requirement or a fuzzy logic type of decision. in this example the user is asked develop strategies of baiting, charging, wait and strike.
Baiting strategy: One monster takes a risk so that the rest can catch up. Once the player is hit, the monsters are usually able to bounce the player back and forth for a few hits before it regains enough control to start pursuing a new monster. Charging strategy: If the player moves forward after being hit, the monster that hit it can rush the player and hit it again in mid-swing. Being hit cancels the players swing and knocks it back again. Wait and strike strategy: Another effective strategy learned by multi-objective neuroevolution in this domain. This approach involves waiting for just the right moment to rush in and hit the player.
baiting strategy
User
Bat
NPC
charging strategy
genetic algorithm fitness environment
wait and strike strategy action Observation
neural network
Human-machine interaction Machines, by nature, are deterministic structures of gathering and processing information, while a human is by nature unpredictable. To set the basis for the conversation about human-machine interaction we ought to underline the characteristics which make them unique. For the interaction process, we need both parts to react to some internal consistency that cannot easily be created but can emerge. One of the vital components of interaction is communication-based on a common language (verbal or non-verbal) and to some point a factor of unpredictability created through the complexity of the communication process. Machines nowadays lack this kind of complexity and are very deterministic, reacting only based on rules with no interpretation or ability to change the outcome and reprogram themselves. So, the next step of machine evolution is to enable them to re-program themselves and optimize themselves based on the information around them.
Machine
Human
Deterministic
Unpredictable
Interaction
Communication vs. Interaction
Communication Expectation
We ought to elaborate on the small differences in the concepts of communication and interaction to truly understand the interaction between humans and machines. Communication is the process of sending and receiving messages, so the parties involved, while cooperation is a step more than communication. In this case, each party acts expecting a specific reaction, where we have actors with personality and dynamics who bring about the play, playing the particular parts out.
Anticipation
Anticipation vs. Expectation Anticipation is knowing the outcome and expecting it to happen, while on the other hand when we are talking about expectation the parties involved do not see the result but have an approximate estimation.Basically, in machine programming, everything is being anticipated and expected. What we need to achieve interaction between human and machines is providing a level of indeterminacy.
Nietzsche’s Labyrinth Deterministic structure with unexpected outcome
The Labyrinth The way to add unpredictability to the machine is to add complexity to its structure, and regarding philosophical background, Nietzsche's Labyrinth is providing the answer of how we can achieve this behavior complexity. The labyrinth is a deterministic structure with an unexpected outcome.
s e lf -ad a p t a t i o n Self-adaptive systems are systems that can adjust their behavior in response to their perception of the environment and the system itself. These diagrams show identical parts which shape self-adaptive systems. It mainly can be divided into four sections; each section is needed to model a working self-adaptation: goals, changes, mechanisms, and effects. For each part, its parts need to be investigated to be able to achieve a balanced system in the end. If there is an error in one part of the system, it's going to fail to run other parts that mean that the system needs all main components and subsystems to work correctly to run efficiently.
Self-adapting System
Goal
Change
Evolution
Flexibility
rigid constrained unconstrained
Effect
Type
Criticality
Type
Autonomy
Predictability
Frequency
Organization
Overflow
Anticipation
Scope
Resilience
Source
inside outside
Goals The goals are the targets which the system aims to achieve. They can be either lifetime long or event-driven. There are two basic categories of goals - the main and the subgoals which support the main goals.
Mechanism
T
functional non-functional technological
T
parametric structural
autonomous assisted
alive dead
prediction result
Change The results of adaptation change. The systems need to decide if it is needed to adapt or not whenever there is a change in the systems environment. Changes could affect only the result of system or the principles of how a system works.
Mechanisms Mechanisms are the reaction of the system towards change. That means mechanisms are the heart of the adaptation process. They can either be activated or become passive according to change.
Effects Influences of any adaptation of the system are the effects of adjustment. Both Mechanisms and Effects are reactions. However, the main difference between them is that the Mechanisms are related to adaptation, while on the other hand, Effects are related to the system where adjustment happens.
Duration
Multiplicity single goal multiple goals
Dependency dependent independent
high frequency low frequency
planned unplanned
single control multiple control
local scope global scope
insignificant system failure
resilient vulnerable
Duration
Triggering
early response late response
case studies MAV Self-navigating robot by Cornell University Through the Use of machine learning to predict drift to allow MAV aerial vehicles to adjust its flight path to maintain the desired course. Once the data is collected and logged; a post-process makes it more suitable for digestion by the learning algorithms. First, they remove all duplicate entries and stale entries caused by network delays; then they compute the position derivatives (velocities) in pixels per second for both the x and y directions and it is this data that they learn on.
The MAV is able to locate its position using a wii controller.
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Black Starfish Damage resilient robot by Hod Lipson, Cornell University
A robotic system that can recover autonomously from unanticipated damage without preprogramming. Through a continuous process of self-modeling, The four-legged eightjointed machine generates actions and uses the resulting actuation-sensation causal relationships to infer its body-schema. It then uses this body plan to create forward locomotion. When a leg part is removed, the same process adapts the internal selfmodels, leading in turn to a generation of an alternative gait.
process of self-modeling.
Mole cube Modular robotics by Festo The project aims to develop open modular robotic framework envisioned as a universal, robust, and low-cost alternative to a variety of specialized robots with fixed body structure and functions. Mole cubes were developed by Cornell University and later sold to Festo The cubes are now self-aware of their structure, and they can assume the concept of roles they are described as evolving self-replicating machines.
self- assem bly Self-Assembly is a process which through local rules components of a system can reconfigure themselves to form an organized structure or a pattern as a consequence of specific instructions or pre-set parameters. There are two main types of self-assembly static or dynamic. Static self-assembly the organization of the structure forms a system equilibrium reducing its energy consumption. In dynamic, patterns generated from the components are organized by local interaction are usually called self-organized rather than self-assemble.
case studies MIT M-blocks The MIT M Blocks have a novel ability to work together and provide support for each other. A novel self-assembling, self-reconfiguring cubic robot that uses pivoting motions to change its geometry. Each unit can move independently. The units achieve these movements by transferring momentum accumulated in a self-contained flywheel to the body of the unit.
Magnetic dice These dice blocks were one of our first research interest for their ability only to connect discreetly. When the glass container they are inside is shaken, the dice can arrange them self to how they were programmed. In this way, they exhibit targeted self-assembly.
Magnetic pattern Correlated magnetics in a brand new research in the fields of magnetics that can print patterns on magnets. That means it can deliver both north and south poles on a single element. Through the use of these technology magnets can have a bespoke preference of connection, and assembly these means they will only connect to a specific side that they are designed to.
I n it ia l Ex p e r i m enta ti on
Rotatable Joints vs Fixed Joints This two models had the most flexiblility versus controlled interaction and only matching types can connect and lock together.
female male female male
// bespoke connections // controlled form behavior // complex variability
// controlled connection // complex variability // bespoke behavior
Flexible Behaviour We were interested in flexibility which we later translated into a physical prototype that can produce volume and aggregate in an orderly fashion. This model allowed us flexibility in capacity, control, and connection.
system over view
system over view The system is composed of aligned research. We started with self-assembly and selfawareness. This strand of research gave us the magnetic patterns to have discreet magnetic patterns to connect explicitly face to face. Which evolved into mobility and fabrication of the single cell. Later we add more of collective behavior and how they interact, assemble and settle.
collective behaviour self-assembly
large population space making
fabrication behaviour
communication
cell behaviour
system
self-awareness aggregation magnetic patterns face to face connection
self-structuring thesis
mobility settling behaviour
single
collective
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
m agnetic patter ns
self-structuring
space making
m agnetic patter ns A specific complex magnetic force field is generated from a magnetic pattern which is a combination of a certain number of positive and negative poles. As a result, the interaction between two magnetic patterns is no longer straightforward attraction or repulsion but is performed as a unique way of connection. In other words, one specific pair of magnetic patterns can define its interactive behavior. Cells with magnetic patterns then are enabled to perform more complex and detailed behaviors in self-assembly processes.
Attracting
Relationship Relationship
Repelling
Relationships and Interactions
Rotation vs Locking
The direct and indirect relationship between different combinations of patterns. Following behaviors were noticeable: rotation, controlled rotation, locking, attracting and repelling.Five magnets pattern does not provide high resolution. Attracting Repelling
The ranking diagram shows the capacity of rotation versus the ability to lock.
Interaction Interaction
Relationship
Interaction
Flexibility
Repelling
180 degree
170 degree
80 degree
Fixed
Locked
Stability Repelling
One-point connection
1/2 Overlapping
2/3 Overlapping
Overlapping 4-points locked
Overlapping 5-points locked
Positive pole (+); Negative pole (--); K05-00 Total number of poles: 5; Number of positive poles: 0; Number of negative poles: 5;
Five magnets patterning We have found numerous combination that showed behaviors of instability, rotation, and flexibility of form. The distinguished behavior of controlled rotation, repulsion, and attraction.
K14-00
K14-01
Total number of poles: 5; Number of positive poles: 1; Number of negative poles: 4;
Total number of poles: 5; Number of positive poles: 1; Number of negative poles: 4;
K23-00
K23-01
K23-02
Total number of poles: 5; Number of positive poles: 2; Number of negative poles: 3;
Total number of poles: 5; Number of positive poles: 2; Number of negative poles: 3;
Total number of poles: 5; Number of positive poles: 2; Number of negative poles: 3;
K32-00
K32-01
K32-02
Total number of poles: 5; Number of positive poles: 3; Number of negative poles: 2;
Total number of poles: 5; Number of positive poles: 3; Number of negative poles: 2;
Total number of poles: 5; Number of positive poles: 3; Number of negative poles: 2;
K41-00
K41-01
Total number of poles: 5; Number of positive poles: 4; Number of negative poles: 1;
Total number of poles: 5; Number of positive poles: 4; Number of negative poles: 1;
K50-00 Total number of poles: 5; Number of positive poles: 5; Number of negative poles: 0;
Relationship
Connection
Connection
Seven magnets Patterning A more complex interaction was achieved by adding two more magnets to the pattern. The combination of the new patterns allowed more stability and variation to the system.
Positive pole (+);
Rotatable Patterns
Negative pole (--);
1-by-1 pattern
Rotation:
Attracting
0
30
60
90
120
150
180
30
60
90
120
150
180
30
60
90
120
150
180
30
60
90
120
150
180
Repelling
Stability:
Patterns
Relationship
Interaction
2-by-2 pattern
Rotation:
0
Stability
Stability: 60
3-by-3 pattern
Stability
Rotation:
0
30
Stability
Stability:
6-by-6 pattern
Rotation:
0
Stability
Stability:
Through physical prototype the behaviour of rotation were explored.
Initial state: Rotation: Number of Attracting Point:
0 degree 38
Rotating 360 degrees Recoding and updating the best results
The model rotates to find the right connection.
Best solution: Rotation: Number of Attracting Point:
24 degrees 41
The connection of the two models is achieved after the rotation of the model placed on the bottom.
positive pole (+);
negative pole (--);
attractive force
repulsive force
Controled Assembly Through the use of an Arduino controlled model, we were able to rotate magnetic patterns and experiment with robotic connection and disconnection of the two models.
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour behavior
cell behaviour behavior
MATRIX DIAGRAM collective
collective behaviour behavior
settling behaviour behavior thesis
communication aggregation large population
DATA
fabr ication behaviour
self-structuring
space making
fabr ication behaviour Through prototyping, we were able to pattern MDF sheets and achieve bespoke connection from one unit to the other. The patterns influenced the overall form of the models and affected their mobility and how they interact with the other groups. Through experimentation, we established the nesting behavior of the cells and enable them to stretch and change. The units evolved as a result of the fabrication technique.
Pattern
Behaviour
Pattern A - the model showed remarkable gravity resistance and agility.
Pattern B - the model showed it can lock in position but it cannot climb.
Pattern C - the model can lock better than pattern B. and it cannot climb as well.
Pattern D - the model can easily climb and it doesn't need a lot of force.
Flexibility Pattern
Behaviour Flexibility
Flexibility
Flexibility
Flexibility
e6
a3
d6
c6
b1
c4
c1
c2
d5
c5
c3
a2
b3
b2
a1
Mobility
Flexibility
e6
a3
a2
d6
c6
b1
c4
c1
c2
d5
c5
c3
b3
b2
a1
Mobility
The latest update showed an interesting nesting behavior where the form was able to compress in size and fold into one and other.
Motion 0_
1_
2_
3_
4_
5_
6_
7_
8_
9_
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
m obility
self-structuring
space making
m obility The single cell is 10 cm by 10 cm, and it weighs about 16 grams without the inside mechanism. Each cell can roll in four directions and climb one on top of the other. The shape and form were refined to get the best outcome for it to achieve mobility. Two different prototypes were developed to explore the behavior of rolling. The change of material was necessary to achieve the desired durability; therefore a new 3D printing technique was introduced to the fabrication.
Each cell is fabricated as a single foldable sheet; it can be laser cut, 3dprinted and casted. The changes affect the quality and the technical ability of the cell which is also affected according to the pattern and thickness of the material. We spent a lot of time tweaking the material thickness and cutting patterns to get the best performance in terms of mobility, self-assembly, flexibility, and durability.
cell patterns These are the patterns, which were developed until reaching the final pattern that performed better in mobility, flexibility, and stability.
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8
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4
3
2
1
6
Mobility The mobility of the cells is shown in both the processes of deployment and selfstructuring. The flexibility and the stability of the exterior shell are necessary to achieve the capacities of rolling and climbing. The best example is the one all the way on the right.
Stability
Flexibility
Mobility prototype V1 The mechanism was developed to achieve mobility. The upper servo rotates continuously while the bottom one dictates the direction.
main rotation gear
single servo skin connection
single shaft
main weight rotation second servo for rotation
Mobility prototypes V2 We improved the mechanism further to accomplish rolling in all directions. We were able to accomplish accurate movement in all directions and using four continuous rotation servo for rotation, and two small servo's to perform the rotation.
second servos for rotation
main weight rotation
Servo for mobility
main rotation gear
skin connection rotation gear path
The mechanism was designed to move and turn to four directions precisely.
Mobility prototype behaviour
interior Soft Membrane 0
30
5
35
10
40
15
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20
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25
55
The interior skin is made of rubber to introduce elasticity to the prototype.
Exterior Semi-Rigid Shell Fins on the exterior shell allow the flexibility of the shape while maintaining the stability.
Mechanism
Mobility Mechanism
The inside mechanism can expand and shrink along three axis. By changing the inner weight of the cell, the center of gravity shifts its position so that the cell can roll.
Mobility of more than one cells
singe cell rolling by itself
neighbor 0 neighbor 0
neighborhood connection neighbor 1
neighborhood connection neighbor 1
rolling together to the main structure
Mass movement A group of cells has the ability to move. They change their shape collectively to roll, climb, collect energy and become stable again.
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system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
cell behaviour
self-structuring
space making
cell behaviour In the cell behavior, we focused on the cell as a single unit and how it can perform regarding energy collection, shape-shifting, and climbing. For the energy collection, we built a prototype to show how each cell can open itself to collect energy. In the shape-shifting, we showed how each cell could change its shape to suit the task it has to perform, and in climbing, we showed how the cell could climb to self-assemble.
Futuristic model Each single unit is aware of its functions and can transform its shape according to its needs.
flexible interior skin rigid exterior shell electromagnet
linear actuator graphene battery hydraulic system tripod joint solar panel
Assembly process
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Metamorphosis Each single unit is aware of its functions and can transform its shape according to its needs.
Self-assembly The self-assembly prototype is built to climb one cell on top of the other. This prototype achieves its target by climbing without assistance. The prototype used linear actuators to achieve this motion.
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Self-assembly
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Energy collection Cells can open their skin to harvest and collect energy.
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Folding and unfolding in sequence
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This catalog shows different varieties of open and closing of the cell surface. And the range of possibilities and how each can adapt to it's surrounding.
Three cell self-structuring The organization of the cells is both an important part of the system and how it can react to change. All the cells can communicate and connect to share mobility elements. They also have a lot of flexibility regarding how they move around.
squishing
rotating
rotating squishing
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The system 1 Rolling
4 Assembly
Cells roll to site.They can move both individually and collectively.
The behaviors of the cells are biased by patternbased local rules.Cells can make collective decisions to achieve specific goals while maintaining the stability of the structure.
2 Sorting
5 Reconfiguration
Cells come in waves. The first wave of cells will sort it self into the basic shape for assembly.
The system is aware of redundant structures. Redundant cells can separate themselves from the main body, and the whole system will start the process of reconfiguration.
3 Waves
6 Migrating
Waves of cells come to the site and climb on top of each other according to local interactive rules to generate structures and shapes.
Cells will migrate to other positions to take part in a new generation of a selfstructuring process.
cell evolution The development of the cell was focused on the accommodation of the two behaviors - rolling and climbing. The shape gives it the ability to roll and simultaneously it can compress and store energy to pop back in its initial shape. This helped us move from one material to the next with the criteria to develop a better shape.
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
aggr egation
self-structuring
space making
aggr egation The previous chapters were focused on what is a single cell and a body plan, how the cells move, climb and create aggregation. This chapter mainly focuses on what is aggregation and how the system could aggregate to create repeatable, examinable and meaningful structures. We used pattern based aggregation logic to achieve these goals.
Magnetic Connection diagram Magnetic pattern
Cell pattern
Wave pattern
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There are three types of patterns in the system: Magnetic patterns, Cell patterns and Wave patterns. The Magnetic pattern represents a digital imitation of the different kinds of magnet configurations which have been explained in the initial research. The Cell patterns are combinations of different Magnetic patterns on each face of the cells and Wave patterns are simple represetation of cell patterns.
Basic possible connections
Magnetic patterns
Wave patterns The possible basic connections that can be produced by the aggregation machine are shown here. The same simple aggregation is shown with aggregation patterns and with magnetic patterns.
Aggregation logic evaluate surfaces
pattern 0
range == 300
pattern 1 pattern 2 pattern 3
rejected
overlape
pattern 4 pattern 5
rotate
new waves existing cells
4
1
checking collision 0
5
1 4
3 2
self
2
3
5
0 4 1
2 3
fitness
best solution distance
angle
angle [ 0 , 180 ]
fitness
fitness
range < cellSize
0 0
distance ( 0 , 300 )
0
angle [ 0 , 180 ]
Fitness = ( 300 / distance )^2 * angle
range < cellSize
distance ( 0 , 300 )
İnitialization shapes
The same aggregation represented in three different forms to easily compare, examine and understand.
Cell dispaly
Cell display Cell pattern representations show how magnetic patterns work with each other in the same structure.
Wave display
Wave display
Line display
Wave pattern makes patterns trackable and distinguishes with ease. It allows understanding how patterns spread through a structure and how different patterns work with each other. Sequene of Patterns
Line display Line conenction shows where the structure starts and which Cells are added to structure more earlier or later. By using line display its easy to track motion in the structure.
The catalog illustrates, a pattern sequence used to create aggregation, the initial shape of aggregation and different type of displays of a result. The aggregation logic is based on patterns and whenever the patterns sequence changes the aggregation behavior also alters. The initial shapes do not affect the aggregation logic. However, they change the starting point of the aggregation and effect the final result indirectly. Different kind of displays enables analysis to get various data to understand the behavior of the sequence of patterns.
Pattern-Based Aggregation
Pattern-Based Structures
Fitness Result
flexibilty porosity speed
speed
12
56
speed
92
flexibilty porosity
11
porosity
15
porosity
35
40
flexibilty
24
flexibilty
72
37
14 27
speed 92
flexibility porosity speed
flexibility porosity speed
12
flexibility porosity speed
15
flexibility porosity speed
11
14 24
72
27
35 40
56
flexibilty
99
57
flexibilty
porosity
22
porosity
speed
42
speed
flexibilty
97
porosity
18 49
37
18
speed
99
flexibilty
89
porosity 42
19
speed
35
97
flexibility porosity speed
flexibility porosity speed
flexibility porosity speed
flexibility porosity speed
89
18
18
19
22
35 42
57
42
42
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
Fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
self- str uctur ing
self-structuring
space making
self- str uctur ing The self-structuring strategy is based on the real-time self-evaluating processes and decision-making logics, aiming at generating stable structures and providing architectural spaces. A large population of cells is involved in this space-making process, each of which is aware of the local conditions including the stability of the cell itself, its neighbors as well as that of the cluster to which it belongs. Every single cell is also aware of the global goals that are assigned to the system and is capable of making the optimized decision that benefits the system most while keeping the stability of the structures.
face-to-face connection
space of posibilities
edge-to-edge connection
locked
locked
neighbor on top == true
number of direct neighbor == 0 self.position.z == 0
2
5
8
11
14
17 26 1 4
20
23
1 19
9
locked
10 19
0
25 9
21
21
15 24
number of direct neighbor == 2
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6
locked
22
3
18
15
23
16
25
12
6
20
13
7
22
16
number of direct neighbor == 1
11 26
4
7
0
14
17
10 13
3
2
5
8
24
number of direct neighbor == 0
self.position.z > 0
number of direct neighbor == 1
number of direct neighbor == 2
locked
self
direct neighbors
self
indirect neighbors
direct neighbors
For each single cell, six direct neighbors are face-to-face connected and twelve indirect neighbors with edge-to-edge connections that are considered as less stable and more flexible.
number of direct neighbor == 2
decision making logics The real-time self-evaluating and self-structuring process is based on a series of decision making logics. leader
support
stable unit stability
unstable
unstable
stable
single seed
structure stability
no obstacle unstable
stable
Each cluster is aware of the stability of itself and is able to decide whether it is a leader or a support.
static target
finding leader
support
leader
000
001
002
003
004
005
next target
current target obstacle
single seed
single seed
solid obstacle
no obstacle The system will be asigned with another target once it reaches the current one.
preset targets
Through introducing obstacles into the field, cells start to create arch-like structures.
static target
000
001
002
000
001
002
003
004
005
003
004
005
closest leader target 0
seed 0
target 1
farthest leader
seed 1
seed 0
event-driven bifurcation: 1_ the size of the cluster is bigger than 100. 2_ (leaders only) there are more than one targets in the system. 3_ (supports only) there are more than one leaders that are unstable.
bifurcation
bifurcation
no obstacle
solid obstacle The bifurcation of a cluster is a event-driven behavior. It is related to both the size of itself and the stabilitis of the leaders nearby.
static target
Seeds in different clusters can also interact with each other according to the real-time positioning information.
no target
000
001
002
000
001
002
003
004
005
003
004
005
nearby clusters target 0
attractor 0 target 1 nearby clusters
temporary scaffold
attractor 3
attractor 1 nearby clusters
nearby clusters
attractor 2
bifurcation
bifurcation scaffold static target
scaffold
A temporary scaffold is built during the self-structuring process. Cells in the scaffold will migrate once the structure becomes stable enough to stand on its own.
Instead of chasing preset targets, every cluster is growing towards dynamic attractors whose moves are biasd by the sizes, position sand stabilities of nearby clusters.
dynamic target
000
001
002
000
001
002
003
004
005
003
004
005
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
settling behaviour
self-structuring
space making
settling behaviour When cells are connected they form a body plan. We focused on how this body plan reacts to external forces- such as gravity. And how they can create and organize as a whole body. The cells can change their flexibility to get better stability. And as a large body, they can react differently to change of structure.
Structure logic Aggregaton
Settling
Movement
The structural experiments we conducted to develop a logic for structuring.
Base
Wall
Over-hang
Soft cells Soft
Rigid cells Rigid
Semi-rigid cells
Semi-rigid
Arch
Create a stable base of more than four cells.
Only when a cell is attached to at least two others a new cell can climb.
deformation
Column
Create a stable connection to climb
deformation
More structure is added for support as the assembly increase in height.
deformation
Wall
Each cell needs four direct neighbors before the beginning of the aggregation.
deformation
deformation
deformation
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
thesis
self-structuring
space making
content 0- Studio Agenda
Thesis Statement
1- Real-Time Data
A- Enviroment B- The city
D- Energy
2- Our System
A- Event predection B- Mobility C- Self-assembly D- Transformation E- Reconfiguration F- Energy G- Materials
thesis
Behavioral Complexity - Studio Agenda "Behavioural complexity" is addressing generative and behavior based design; self-aware/self-assembled systems; participatory/cybernetic frameworks, mobility prototypes, and robotics. Behavioural, parametric and generative methodologies of computational design are coupled with physical computing and analog experiments to create dynamic and reflexive feedback processes. New forms of spatial organization are explored that are neither type- nor site-dependent but instead evolve as ecologies and environments seeking adaptive and hyper-specific features. This performance-driven approach aims to develop novel design proposals concerned with the everyday. The iterative methodologies focus on investigations of the spatial, structural and material organization, engaging in contemporary discourses of architecture and urbanism.
How it works:
Cities are fixed and immobile. Architecture space typology hasn't changed for the past 200 years. Change in the city is a top-down machine that generates direction in the city daily life which takes a long time to implement. This thesis propose a new architecture typology that can address an on demand urban system. Through the use of live data analysis and a predication system we can study the city needs and respond to its requirements before it can happen.
Through the collection of the city existing data, forecasted data, live data, studies from research groups and valuable statistics the system can respond to city driven events and create the required space. Through big data analysis and data mining, the system will predicate and forecast future events and respond to this information. It can also act as an emergency response system. Where in certain types of congestion the system can act as a universal organization mechanism. Mobility: The system will have two modes of mobility globe and local. In the global scale, cells will work together to achieve long distance movement. Locally the cells will use the behavior of the tribe to travel as a group to the selected destination nearby and in case of a much longer distance travel the system can deploy from all means of transportation, choosing the most efficient one. For example, the system can be stored on airplanes containers as flat sheets for longer distance travel. Deployment: Sixty percent of the system is packed as flat sheets with the mechanisms deployed separately. In a box of four hundred sheets, two hundred mechanisms can deploy and activate the four hundred flat cells to assemble. During the assembly, the system is partially stable while other are adaptable to change in weight, structure, and function. Each cell can change its geometric shape to accommodate the type of task it has to perform. Transportation, packing, self-structure, and stability. Assembly: The system is always adapting, updating and reconfiguring. A tribe of cells that travel together to conserve energy. As they arrive to the space they disengage to initiate the assembly. The lower layer form a lower stable layer where they are able to give a stability plate to the whole system. The cells are able to change their rigidity to accommodate the overall system stability. They are also able to adapt to the change and respond to the demand on the system. A new urban Typology: Cities have not changed much for the past 100 years. Modes of transportation have changed, but spaces are still the same. For the information flow between the city control system and the demand is usually a top-down process but we are proposing a bottom-up approach where the system can analyze the need and respond to the city requirements through a prediction mechanism. Space today is usually used for a number of activities while the activities and program change the space itself is the same. We propose a different type of space where the user is in control of the parameters of the space and its existence on all scales. Scenarios: We envision different types of scenarios. An airport that requires temporary housing for overcrowded delays, a disaster scenario where a lot of individuals are being allocated to other locations and want to get living spaces. A pop-up event in the city either it's a marketplace that is constructed daily or a scaffolding system that can deploy different temporary structures to meet. A wedding event where a temporary structure is required, training for company personnel that requires classrooms, a replacement for underconstruction zones.
Real-Time Data Analytical processes that used to require month, days, or hours have been reduced to minutes, seconds, and fractions of seconds. A rapidly emerging universe of newer technologies has dramatically reduced data processing cycle time, making it possible to explore and experiment with data in ways that would not have been practical or even possible a few years ago. Real-time big data isn't about storing data in a data warehouse it's about the ability to make better decisions and take meaningful actions at the right time. Real-Time Data: information that is delivered immediately after collection. There is no delay in the timeliness of the information provided. Real-time data is often used for navigation or tracking. Some uses of this term confuse it with the term dynamic data. Big Data: A collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. The challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations.
Environment
Ten years ago live data in our environment was strictly for the weather, wind speed, direction, and rain prediction. Today this amount of data has exploded, and it’ s being updated instantly every minute. A few are wind-speed, air pollution, pollution, Risk flooding, coastal Erosion, river water quality, industrial pollution and landfill amount are only a few of those being uploaded online. While some are updated every minute, others are updated annually and sometimes daily the amount of data projected every day about our cities is increasing every day.
http://www.londonair.org.uk/LondonAir/Default.aspx
Sun Data
Wind Data
http://earth.nullschool.net/
The City
LIVE Singapore 1
2
3
4
5
6
Wiki City Rome- Dye Sensitized Solar Cell (DSSC)
Events location
Star 1
Atac bus
Star 2
Cellphone usage
The Wiki City project represents the development of the Real Time Rome project, presented in 2006 Venice Architecture Biennale. Then, real-time data was displayed during the exhibition at a geographical distance from where the data was collected, presented to the audience. The Notte Bianca implementation allows people access to the real-time data on dynamics that occur instantly in the very place they find themselves in, creating the intriguing situation that the map is drawn by dynamic elements of which the map itself is an active part.
The WikiCity is interested in how do people react towards the new perspective upon their city while they are determining the city’ s very own dynamic and how does having access to real-time data in the context of possible action alter the decision making in how to go about different activities. The project includes the active uploading of information by citizens, local authorities and businesses regarding an everincreasing field of data; an elaborate approach to semantic data structures to enable novel ways of querying the data and a rich array of multimodal access interfaces for users to interact with the data in a meaningful way.
1 2 3 4 5 6
Isochronic Raining taxis Urban heat islands Formula one city Ral-time talk Hub of the world
proportional deformations to travel time combining taxi and rainfall data to match taxi supply and demand estimated temperature rise and energy consumption amount of text messaging during the Formula One race level of cellphone network usage port and airport's global reach
LIVE Singapore aim is to create an ecosystem of urban dynamics. It creates a feedback loop between people, their activities and the city. Thus, the data is given back to the citizens who themselves generate it through their actions and in this way provides valuable information about the actual state and dynamics in the city. The project focuses on an urban real-time data platform, flexible and accessible API, interface and interaction models for the real-time data platform, query, as well as search and visualization tools for urban data. The aim of the project is not only to create a single application but to design toolbox that describes the urban dynamics. Anyone can access a different kind of real-time information concerning the city, like taxi location, isochronic map, urban heat island and real-time talk through the open platform which collects, elaborates and distributes real-time data that reflect the development of the city.
Data providers telecom operators public transport (bus, subway, train) companies businesses local authorities private individuals
Infomation phone calls, text messages vehicles' locations, paths, time schedules real-time location information of vehicles location-time sensitive services/products upcoming events, environmental conditions general interest, events, requests or offerings
Mobility
The Tube is the heart of London. The London Underground is thought to be the third largest metro system in the world, regarding miles, after the Beijing Subway and the Shanghai Metro. By gathering live data from the tube location, we can estimate the number of people traveling.
0
01
02
03 The bus is the oldest form of public transportation in London. There are currently 1,311,276 million Freedom Pass holders in London. They can ride the bus. Buses use GPS to track the 8,765 buses in service in London.
04
05
06
Live data is being collected through tracking of pedestrians in cities.
The Crossrail is the extension of London’ s underground. It connects the city of London with other cities as well as the suburbs of London. During 2011/12, London Underground carried a record number of passengers, with 1.171 billion journeys made.
On top of that London has the most open source of data regarding station access, number of users and the history for eight years back. Which is free to use and online.
Energy London has bold ambitions in the energy sector, with a goal of reducing emissions to 60% below 1990 levels by 2025. But London today relies heavily on fossil fuels and just 2% of total energy comes from renewable sources.
Three-quarters of London’ s energy consumption is fueled directly by oil and gas, with the vast majority of the remainder generated by fossil-fuel-powered electricity. Just 2.1% comes either direct from waste and renewables or renewably-generated electricity.
2.4 Transport 1.3 Transport 0.4 industrial
2.2 Housing
1.9 Public sector
2.7 Household Energy
1.1 Built Infrastructure
0.6Housing Infrastructure
1.2 Food
1.3 Private Services 1.3 Public Sector
1.5 Consumer Goods
London's CO2 emissions per capita are higher than the world average but lower than those of comparable developed cities New York and Portland, as well as Singapore. The landfill is gradually being phased out as a method of solid waste management, but still accounted for roughly one-third of the total in 2011/12. Incineration and recycling/ composting also make up around a third each.
Event Prediction
Potential Sites - Public Spaces
In cities like London access to data is within a click of a button. The updates for this data ranges from annual, weekly, daily and per min. With such data we can find patterns and treat it using theories and ideas such as big data analysis. The idea is to create a probability map from the historic data and the real time data together you are able to get a best guess solution to find the location of the next event that will happen.
Collecting big data
Data mining
Defining event pattern
publich spaces in London
Public spaces, like squares and parks, are considered as potential sites for our system to operate. By synthesizing real-time live data with the multi-layer probability map, our system is able to predict events that will happen at these locations, so as to guide the movements of cells. The correction of the prediction will be recorded to modify the data base and to update the probability map.
Case Study The Los Angeles Police Department now is using cutting edge technology algorithm at The Real-Time Analysis and Critical Response Division in downtown LA to predict future events in the city. Multi-layer probability map generation
Predicting event locations
Sending result to cells
What algorithm does is basically using years or sometimes decades of data of crime reports and find similar points of every each event and how it triggers the next one. Through this network building process, system is ready to generate high probabilities for different kind of crimes and locations and send this information as small boxes on maps.
Principles We would like to apply prediction algorithms into our system to be able to not only react according to live data that are collected, but also to actively participate into the urban environment. Event prediction algorithms will push us further than the time we are in and give us a multi-layered probability map that shows possible future events to work on it. Every each layer of the probability map will demonstrate specific possible events in a short future. According to this probability map system will define where it should direct itself and what kind of event it wants to add to urban. The algorithms are capable of predicting the events planned in external sources, as well as the ones which are not planned by anyone but are possible to happen. Our system will perform this task and re-locate itself to needed areas with the city when its needed and also design the missing events which is possible to happen in city.
crime
data base
algorithm
prediction
result
correct
revision
false
The process of refining pattern in previous events for predicting the future actions is called predictive analytics. Predictive analytics is combination of statistics, computer science and operations research, and it is used in reducing fraud, waste, abuse and automates manual process to achieve smarter decision at the end of analyzing vast amount of data.
Mobility
passive cell movement by rolling
Data is collected and analyzed into every cell. After the data is stored into the model it is ready to start rolling and searching for other cells in the vicinity. The cells have the ability to communicate via signals and are refining together the decision-making process based on the data they have. The next step is the collective mobility of the models to the location. In the end the structure is built through the movement of the cells, which is based on the information they have about the city. data analysis
local communication
movemet by climbing leader
Re-configuration
collective mobility
state A
The mobility of the cells is achieved through two basic movements - the rolling and climbing of the cells. Their ability to roll collectively and having a leader allows for high energy efficiency. As far as re-configuration and global movement are concerned climbing is vital. The system can easily re-configure and transform according to the data provided from the environment. Global movement allows a big amount of cells to move.
state B
Global movement
Cell’s movement
neighbour 1
1
2
3
4
5
6
neighborhood connection
single cell movement
neighbour 2
rolling together to the main structure less enegy consumption
Self-healing polymer
Self-Assembly SELF-ASSEMBLY
Principle Roombots On the other hand Roombots project is developed as self-assembling , self-reconfiguring and moving furnitures by small building blocks. Roombots are simple modules that can attach and detach by the help of connectors between units, and it allows structure to be arbitrary and in motion.
Self-assembly is a term to define a natural phenomenon which based on the informations stored in every individual components to be able to build patterns and structures.The assembly means “ build up or put together “ and the self” implies “ without any guidance and help or on its own” .It is also a bottom-up production process, meaning that it start with small pieces and organize a larger structure. The core of self-assembly is possible to be observed as a natural phenomenon in nature in nanoscale formations. Later on engineers, designers, scientists focus on implementing the idea on various scale of research. Even researches mostly done in labs and nanoscale level at the beginning, now its possible to see physical object using self-assembly logic to perform their tasks. Self-assembly is a term to define a natural event which based on the informations stored in every individual components to be able to build patterns and structures.
Their ability to re-configure enable them to come together as different formations, these formation could be either static or mobile. Roombots also change their motion pattern which is most suitable to it’s current form, tripod structure on left side have a motion sequence looks like a three legged creature , however on right hand side after transformations , new compositions moves more like a snake. In the core reconfiguration is not only changing robots typology but also change their behaviour of motion, and every each motion has it’s own characteristics like speed, motion space and ability to climb.For our purpose collabration between our cells to perform re-configurability to change their formation to divert their behaviour according to significant task is crucial.
We are locating self-assembly into center of our responsive system to real time changes in urban city. That is the most convenient way to give an answer to flowing data through self-assembling robotic systems. There are three main parts we interested in self-assembly systems ,versatility : the ability to assemble and disassemble to self-structure various type of morphologies according to different kind of tasks , self-sufficiency: to be able to perform task with it’s own mechanisms without any supervision or help from outer source and becoming foundation of multi-type collobrative actions as they are able to create different kind of forms. There are three project which represents differents parts of our motivation towards self-assembly , Self-folding origami robot, Roombots, Kilobots.
Self-folding robot The self-folding robot is a contradicting approach to produce a robot when there are a lot of proven ,high-tech manufacturing techniques. Precious part of this research project is letting robot assemble itself autonomously to let it perform some functions. It is made out of composite papers and polystyrene as sheets and flexible electronics. This reserach shows that it is possible to form three dimensional functional robots out of two-dimenstional sheets and it opens opportunity to transfer robots as sheets, save from space and cost of manufacturing three dimentional of parts of robots. In our system we would like to get informed from this research and push it forward to create folding and unfolding complex machines which are able to fold themself into the shape, perform immediately and unfold when it’s need to get packed.
tripod motion
transformation
linear motion
Kilobots
X 1000
Self-organizing thousand robot swarm project Kilobot is another example to self assembly. In nature , billions of small components come together to form more complex outcomes, just like complex organisms. The Kilobots are also interested in collective behaviour without any physical connection. Every individual robots has capability to communicate with its surrounding and be part of the swarm behaviour. The information sended to robots through infrared system from outsource computer, they collectively analyse input and perform individual different action to take their positions in the two -dimensional world. What we want to add to this process is to be able to define their organization rather than getting information from an outer source. So every individual robot is not going to be a part of the performance but also be an active participant to decision making system.
Energy
Application
By embeding dye sensitized solar cells (DSSCs) into its shell, a single cell can collect solar energy and convert it into electric power. DSSCs can work under all kinds of lighting conditions, even under a cloudy sky or inside a room. The solar energy collected by DSSCs is stored by an in-cell graphene battery, which has much higher capacity volume and charging speed than traditional batteries. The energy is used to enable the mobility and self-assembly mechanisms of our system. When single cells connect with each other, the aggregated structure can function as a collective battery system which is able to provide enough electric power for lighting and charging devices. And Artificial light can be absorbed back by cells as energy source. Environment Data
The Energy Collection, by Marjan Van Aubel, 2012
Embed DSSC into units -
+
single cell
enable mobility
Collect Energy from direct light & diffused light
flexible DSSC
Get dye from fruits
Store Energy by Graphene Bttery
Make flexible DSSC
Connect units to collect more energy
Use a cabinet as a battery
Harvest energy to power devices
Advantages use artificial light as energy source enable self-assembly
Use Energy to power devices
Harvest Energy from connected cells
Energy Collection - Dye Sensitized Solar Cell (DSSC)
Sunlight
e
Sunlight passes through the transparent electrode into the dye layer where it can excite electrons that then flow into the titanium dioxide. The electrons flow toward the transparent electrode where they are collected for powering a load. After flowing through the external circuit, they are re-introduced into the cell on a conductive electrode on the back, flowing into the electrolyte. The electrolyte then transports the electrons back to the dye molecules. Electron
Media
Dye
Titanium dioxide
TCO glass
500 Nanoparticles
TiO2 with Dye
Electrolyte
e
Flexible
Different from traditional solar panels, DSSCs can work under diffused light. This ability makes it more adaptive to low-light conditions. In a cloudy city like London, DSSC can help our system to keep collecting and harvesting meaningful amount of energy, so as to maintaining its moving and self-structuring functions, as well as providing power for users.
DSSCs use electrolyte as media, and the electrodes inside are nanoparticles like TiO2. As a result, DSSCs can be constructed by flexible, lightweight and thin materials, such as conductive plastic. These characteristics make DSSCs suitable to be embeded into the shells of our cells so as to collect energy for our self-assembly system.
£
A DSSC is based on a photoelectrochemical system that convert solar energy through chemical reactions.The modern version of a dye solar cell was co-invented in 1988 by Brian O'Regan and Michael Grätzel at UC Berkeley.
Principle
Adaptive
-
I3
3I
-
-
I3+
-
2 e = 3
I
Efficient
0
v
Low-cost
!
DSSCs are extremely efficient at converting photon to electrons. The overall quantum efficiency for green light can reach 90%. can generate about 0.7 V under solar illumination conditions, which is higher than that of silicon panels. And in terms of current, it can offer about 20 mA/cm2.
Energy Storage - Graphene Battery
-
Li
Li
+
Li
Graphite TCO glass
e e e
Unlike traditional silicon-based solar cells, DSSCs require no complex manufacturing processes and the raw materials used are inexpensive, leading to a lower manufacturing cost. On the other hand, DSSCs are strong yet lightweight. By being robust, they require lower maintenance cost.
-
+
-
+
e traditional battery
+
Higher battery capacity & charging speed
+
Lithium battery is the most popular way to store energy. It provide power by transferring ions from the anode to the cathode. The amount of lithium-ion directly affects the capacity volume of the battery. The 3-dimentional hexagon structure of Graphene provides more space to store lithium-ion without increasing overall mass of the battery. Graphene is also one of the most conductive material that can offer ten times faster charging speed.
graphene battery
Reconfiguration
Real-time Reconfigurable System
Architectural spaces are now designed to fit predefined functions, just like a box to contain objects. However, the future use of these spaces is uncertain and changeable. The inner functional distribution of a building may change over a time. It normally doesn’t ask for reconstruction, just like the same box can fit different but similar objects. But such kind of fitness is passive and non-adaptive. What’s more, changes to the major function will normally cause reconstruction or even demolishment.
Data
Demand
Configuration Area Height
User This is a photo of Osaka Stadium. It used to serve as the home ground of a baseball team, with capacity of 31,370 spectators. In 1988, the baseball team was sold to another company and moved to Fukuoka. As a result, Osaka stadium was abandoned and later converted to a sample housing showground. The dramatic change to the major function is far beyond the adaptability of the construction. And in 1998, the stadium was demolished to give place to a new shopping mall project.
Number of rooms
Number
Architecture scale
Age Gender
Space
Accesses
Program Shape
Event
Area
Circulation
Height
Interior scale
Type Duration Changes
Situated in a dynamic built environment where a variety of live information and data are intertwined, architecture should be able to respond actively to these real-time changes to keep its fitness and efficiency. As a result, a real-time reconfigurable system is needed.
Fun Palace, Cedric Price
Windows
Illumination
Fun Palace
Weather
The Thinkbelt was not a single construction, but a network of mobile units that connect to the existing rail lines, roads, and air networks via three major transfer points, which formed a triangle from Pitts Hill to Madeley to Meir, encompassing all the towns inside as part of the campus. Industrial units could be linked or detached according to differing uses – so, in addition to public learning spaces, these units would also offer accommodation for visiting students and staff. Furthermore, three parallel rails can be joined by inflatable walls and portable fold-out decks to offer more space for larger lectures and talks. This would allow blurring between the city, infrastructure and architecture showing the university as an expanding system.
Accesses
9
Amount
Environment
The Potteries Thinkbelt
Duration
Ventilation
Precedent Fun Palace is developed as a transformable machine, which can accomodate a different events. As Price himself laconically noted, “The Fun Palace is a kit of parts, not a building” – one that he doubted would ever look the same twice . "A ‘virtual architecture’ like the Fun Palace would have no singular program but could reprogram itself to accommodate an endless variety functions". As a basic foundation of his vision the architect considered predictability; hence the ability to cater for future needs by altering the building's framework when necessary.
Number of floors
Site Temperature
Type Size
Furniture scale
Position Rigidity
After the self-assembly process, the system keeps evaluating its fitness and modifying its architectural configurations actively and responsively based on real-time data and a series of decision-making mechanisms. Data collection Each cell is able to collect data through its sensors. By sharing local information with each other, cells can collectively set up an understanding of the environment and the users, and keep it up to date. They also have the access to the existing data sources to get live data regarding weather conditions and event information. Demand analysis The system analyzes the data to figure out its architectural demands for spaces, programs, circulation, duration, ventilation, and illumination. For example, based on the number of users and the event type, the system is able to estimate the types of programs and the sizes of spaces that are needed. By comparing the demands with its current condition, the system can evaluate its fitness and make decisions on how to optimize. Real-time reconfiguration After the self-evaluating process, real-time feedback is generated and utilized to guide reconfiguration processes at three different scales, from architecture scale to interior scale to furniture scale. The result of each decision and move will be sensed and evaluated again by the system so as to bias the decision-making process at the next iteration. In this way, the system can be more adaptive to the dynamic environment and real-time changing usage.
The Potteries Thinkbelt, Cedric Price
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
com m unication
self-structuring
space making
com m unication Every cell has the capacity of sensing the environment and reacting accordingly, such as following the light and avoiding obstacles.The communication intelligence is needed when a group of cells tries to interact with each other. They can make group decisions and finish group tasks, such as collective motions and body plan generation.
Single mechanism
arduino light sensor battery proximity sensor servo wheel weight
Basic capacity Awareness
Motion mode
avoiding obsticle
navigation mode
following light
accurate mode
following light and avoiding obsticle
play mode
Complex communication avoiding obsticle
navigation mode
following light
accurate mode
following light and avoiding obsticle
play mode
two connected
movement in formation
three connected
body plan assembly
Two avoid each other
Two groups join
Play mode
Sorting
To demonstrate the capacity of self-assembly, two prototypes are designed based on similar mechanism.
Active cell
The active cell has six linear actuators inside, which enables it to rotate and climb along three axis.
The passive unit can only accomplish 2-axis motion.
Passive cell
Collectively they can finish all the basic motions that are necessary for demonstrating self-assembly.
climbing up
sweeping horizontally
coming down
01
02
03
04
05
06
system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
self- str uctur ing
self-structuring
space making
self- str uctur ing Different self-structuring strategies are applied according to the energy condition of both the individual cell and the system as a whole. According to the global structuring goal, cells make their own decisions. Each cell can select from the three building strategies: the stepping, the shortest path and the collaborative.
Energy consumption logic
Stair-like pattern
A local ruleset found in the self-assembly prototypes was developed. Three time-based building strategies were explored according to the energy consumption of the cells. The first strategy is the best overall out of the three because it has the minimum overall energy consumption. However, the two other scenarios are good if time is essential or, in the last case, if the existing system does not have any energy to spend and can only depend on the new cells.
Building strategy
stair-like pattern
Growning pattern
Energy consumption (joule) minimum overall energy consumption time: 00:00:48:32
active unit existing units overall system
441 504 945
Energy consumption (joule)
minimum overall energy consumption
01
02
03
04
05
06
07
08
09
time: 00:00:48:32
active unit existing units
441 504
overall system
shortest path
945
minimum building speed time: 00:00:29:05
active unit existing units
448 756
overall system
collaborative motion
1204
minimum burden on existing system time: 00:00:41:44
active unit existing units overall system
1603 126 1729
A stair-like structure leads to a minimum overall energy consumption, where the new cell and the system spend almost the same amount of energy to climb. We tested different ideas, and this one was by far the least for energy consumption.
Shortest path
Collaborative motion
Energy consumption (joule)
Energy consumption (joule)
minimum building speed
minimum burden on existing system
time: 00:00:29:05
time: 00:00:41:44
active unit existing units overall system
448
active unit existing units
756 1204
overall system
1729
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If time is vital, there is a possibility that the cell can choose the shortest path. The system responds to the new cell and re-arranges itself to help the climbing procedure. In this case, the system spends almost double of the energy that the new cell does.
1603 126
In the strategy of the collaborative motion, at least two new cells are required. The system almost does not spend any energy. However, the amount of energy required by the new cells is the highest, almost double of the other scenarios.
Vertical arrangements
Stepping
We aimed to achieve the same goal by using the three previous strategies. Vertical arrangements were created according to the time of assembly and the population of the cells used. By following the local level rules applied many times led to these formations.
The stair-like pattern strategy requires the least number of components to reach its goal. However, the time of assembly is the longest of all. 460 cells and 18 min are required reach 200 cm high.
Height: 200 cm Cell size: 10/10/10 cm minimum time building speed
Shortest path cells number: 543 time: 12 min
Hybrid cells number: 474 time: 15 min
Shortest path cells number: 625 time: 17 min
Shortest path cells number: 597 time: 19 min
Stepping cells number: 466 time: 18 min
Stepping cells number: 732 time: 38 min
Stepping cells number: 404 time: 32 min
minimum number of cells
minimum number of cells
Shortest path
Hybrid
The next strategy is the Shortes path. The time of assembly is the quickest of all three. However, it needs almost 100 cells more to achieve the same goal in 12 min.
The Hybrid strategy is better than the previous two because it needs almost the same cell number as the stair-like strategy and the speed of assembly is as one of the Shortest paths.
Vertical aggregation self-structuring
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Base self-structuring
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Horizontal self-structuring
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Furniture re-configuration
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Vertical arrangements Variation of vertical aggregations was created according to the previous logic. These aggregations incorporate the hybrid pattern of assembly.
Spiraling Height: 700 cm Cell size: 10/10/10 cm
7456 372833
7078 348579
7330 387132
7379 235715
5262 272129
5016 221085
6803 339451
6150 304574
8190 399146
5542 292949
Distortion Height: 700 cm Cell size: 10/10/10 cm
7456 372833
7078 348579
7330 387132
7379 235715
5262 272129
5016 221085
6803 339451
6150 304574
8190 399146
5542 292949
Symmetricality Height: 700 cm Cell size: 10/10/10 cm
7456 372833
7078 348579
7330 387132
7379 235715
5262 272129
5016 221085
6803 339451
6150 304574
8190 399146
5542 292949
Branching Height: 700 cm Cell size: 10/10/10 cm
7456 372833
7078 348579
7330 387132
7379 235715
5262 272129
5016 221085
6803 339451
6150 304574
8190 399146
5542 292949
Height: 700 cm Cell size: 10/10/10 cm
Total number of cells in the system = 7379 Total energy spend =897.330.300 joule Total energy collected = 160.120.084 joule Total building time= 2 hours 27 minutes
5x System totally spends energy which is equal to five 50 watt lightbulbs running for an hour
Structure takes 2 hours 27 minutes to build on its own
Cells generate 47 kwh energy from solar panels
Population
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Population
Population of 1000
The population and its possibilities of aggregation to define different types of formations and specifically what a population can do from 40, 100, 200, 1000. 01
Population of 40
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Population of 100
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Population of 200
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Targeted population
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< 100
< 200
< 300
< 400
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system single
self-awareness self-assembly mobility
magnetic patterns face to face connection
fabrication behaviour
cell behaviour
collective
collective behaviour
settling behaviour thesis
communication aggregation large population
space m aking
self-structuring
space making
space m aking M anagi ng a l ar ge num ber of c el l s to c r eate s pac e i nv ol v e r eal - ti m e s tr uc tur e anal y s i s , the s tr ategy c hanges dependi ng on the r equi r em ent of the ov er al l s y s tem . If the s y s tem i s i n ener gy s av i ng m ode, s pac e tak es m or e ti m e. W hi l e i n s hor tes t path m ode ti m e i s s i gni fi c antl y r educ ed, but the ener gy c ons um pti on i s i nc r eas ed. T he s y s tem c an r em ov e r edundant c el l s . T hat c an m i gr ate to other par ts of the s tr uc tur e w her e m or e c el l s ar e needed.
Energy and building strategy
building strategy
building trajectory
building speed
building speed
energy
high energy
shortest path mode
low energy
time energy saving mode
Structural analysis
structure sample
force path analysis
connection
structure evaluation
force path
pressure
importance
vertical force path
tension
horizontal force path
pressure
week point
proportion
force path
structural optimization
proportion
force path
structural optimization
space-making goal
population
coverege / ground occupancy
Goal-oriented reconfiguration
urban furniture
Goal-oriented reconfiguration
Constructing Temperary scaffold is generated to support the structure when they are unstable.
Disassembling Cells that migrate from the disassembled part will go to surrounding structure to reinforce.
Reconstructing
Redundant cells in the existing structure are free to participate in reconstructing process nearby.
final anim ation
AADRL Studio Theodore Spyropoulos Agenda: Behaviour complexity Tutor: Theodore Spyropoulos Assistants: Mustafa El Sayed Apostolis Despotidis Team Members: Ahmed Shokir Cosku cinkilic Pavlina Vardoulaki Houzhe Xu