and Artificial Systems Self-Assembly in Natural Collective Building andarpwhite/courses/5002/lectures/self... · 2002. 4. 1. · heterogeneities. • The social insect activity only

AM, EE141, Swarm Intelligence, W6-2

Collective Building andSelf-Assembly in Natural

and Artificial Systems

Outline

• Collective building and self-assembly in natural systems• Examples• Mechanisms• Modeling• Reverse engineering with GA

• Collective building and self-assembly in artificial systems• Passive bricks• Active mechatronic units

Collective Building andSelf-Assembly in Natural

Systems

Natural Examplesof Collective Building

© Guy Theraulaz

© Guy Theraulaz


twhiteMacro structure is same, micro structure variation -- the template mechanism at work.

© Pascal Goetgheluck


twhiteAgain, very similar to structures of previous pictures. Modules that are repeated again and again.

© Masson


twhiteStructures can obviously be huge -- insects can do what we do with our cities!

Natural Examplesof Collective Digging

Coordination Mechanisms forCoordination Mechanisms forCoordination Mechanisms forCoordination Mechanisms forthe Building Activitythe Building Activitythe Building Activitythe Building Activity

The plan

Environmental template

11

22

Stigmergy33

Collective BuildingMechanisms

The plan


twhiteNo! There's no real plan here except a stimulus response mechanism that identifies local configurations ...

It's the regularity and modularity that's key.


The plan


11

22

Stigmergy33


• The building plan pre-exists in the environment under the form of spatialheterogeneities.

• The social insect activity only outlines these pre-existing environmentaltemplate. Environmental changes performed by insects play a minimalrole in the building activity itself.

• There are several forms of environmental template:• gradients naturally existing in the environment (humidity, temperature …)

• chemical gradients generated by one or more individuals of the colony


Eggs

Larvae

Nest Structure in the Ant Acantholepis Custodiens

3.00 a.m. 3.00 p.m.

Pupae

T

twhiteTemplate mechanism guides the stigmergic building mechanisms -- essentially this is just the interaction of two signals.

Convective Air Flows andComplex Chemical Templates

twhiteGradients can be temperature and chemical ... and other combinations too.

© Guy Theraulaz

Convective Air Flows andComplex Chemical Templates

The plan


11

22

Stigmergy33


• It defines a class of mechanisms exploited by social insects to coordinateand control their activity via indirect interactions.

• Stigmergic mechanisms can be classified in two different categories:quantitative (or continuous) stigmergy and qualitative (or discrete)stigmergy

Stimulus

Response

S1

R1

S2

R2

S3

R3

time

S 4

R4

S 5

R5

Stop

Definition

Stigmergy

Positive feedback and self-organization

Sequence of stimuli and answers quantitativelydifferent

11

Sequence of stimuli and answers qualitativelydifferent

22

The role of the two different stigmergic mechanisms in collective building

Stigmergy

• The successive stimuli quantitativelydifferentiate in their amplitude and merelymodify the answer probability of otherindividuals

• Examples :mass recruitment in antsdead ant aggregation in ant cemeterypillar building in termites

Features

Quantitative Stigmergy

Pillar building in termites

• Termites impregnate with pheromones the building material• Pheromones diffuse in the environment• Individuals carrying ground bullets follow the chemical gradient; they

climb towards the highest pheromonal concentration and drop there theirbullets

• Material drop rate is proportional to the number of active insects in thelocal region (positive feedback)

Spatial distribution of insects and their building activity are locally controlledby the pheromone density.

Quantitative Stigmergy

twhitePatterns of pillars emerge from an (initially) random distribution of materials.

Now lets add another force ...

twhiteGradient is in direction of arrows. We see that a valley appears after some time.

Sequence of stimuli and answers quantitativelydifferent

11

Sequence of stimuli and answers qualitativelydifferent

22

The role of the two different stigmergic mechamisms in collective building

Stigmergy

Self-assembly process

twhitecontinuous = pherome sensing from building materials.discrete = cell building in wasps nests

• Successive stimuli are qualitatively different. Thisprocess generates a self-assembly dynamics.

• No pheromone involved?

time

S 2

B C

•••S 3S 1

A

Features

Qualitative Stigmergy

Nest Building inNest Building inNest Building inNest Building in Polist Polist Polist Polist Wasps Wasps Wasps Wasps

Nest Building in Polist Wasps

Organisation of the building activity

• It is indirectly carried out via the different, localconfigurations a wasp can find in the nest

• A probability of deposing a new cell is associatedto each configuration

S 3

S 1 S 1

S 1

S 1S 1

S 1

S 1

S 2

S 2

S 2S 2

Potential sites for building



Probability of creating a new cell given the configuration of neighboring cells

0 ,0

0 ,2

0 ,4

0 ,6

0 ,8

1 ,0

1 2 3Number of adjacent cell walls

Nest-Building ModelingNest-Building ModelingNest-Building ModelingNest-Building Modeling

Swarm on a 3D Lattice

A swarm of nest builders … agent features

• Reactive actions• Random movements on a 3D lattice, no trajectories• No embodiment (1agent = 1 single cell), no interference

(however 2 agents cannot occupy the same cell)• n agents working together means n actions (not

necessarily n dropped bricks) before next iteration starts;all n agents see the same configuration at a giveniteration

• Agents’ team is homogeneous• No global plan of the whole building• Local perception of the environment

The neighborhood of each agent is defined as the 20 cells around it (7 above, 6around, and 7 below).

Neighborhood Cell contents

X X

z + 1 z z - 1

2

22

0

01

1

2

22

01

1

2

22

0

21

1

Definition of agent ’s neighborhood for hexagonal cells


A

z + 1

z

z - 1

Distributed nest building: several sites are active simultaneously



Examples of building rules

z

Z+1

Z-1

• Building process is irreversible(no cell can be removed)

• Deposit rules can be strictlydeterministic or probabilistic

twhite

Example of a rule set (Polist wasps, 7 rules)


Figure 6.9Swarm Intelligence: Bonabeau et al

Nest-building Modeling

Obtained structures (Polists wasps, 7 rules)

With deterministic rules With probabilistic rules

Looking for Stable Nest Architectures

What kind of nest architectures can we build with the same method?

• What are the rules to implement in order to obtain stable architectures?

• An architecture is considered stable when several runs of a simulationwith the same rule set generate architectures with the same globalstructure.

• Mechanisms of stigmergic coordination reduce the number of stablearchitectures.

Agelaia (13 rules)

Examples of Stable Architectures

Parachatergus (21 rules)

Vespa (13 rules)

Stelopolybia (12 rules)


Chatergus (39 rules) Artificial Nest Structure (35 rules)


twhiteShow animations.

• The building process is carried out in successive steps. The current localconfiguration generate a stimulus different from that of the previousand of the successive configuration (qualitative stigmergy).

• Only this type of building algorithms generate coherent architectures.

• The whole set of these algorithms generates a limited number of nestshapes.

How to build a stable architecture?

Looking for Stable Nest Architectures

Reverse-Engineering: Fromthe Building to the

Individual Rules using GA

Similarities to Controller EvolutionPrey-Predator (GA, Floreano 98) Obstacle avoidance (RL, Kelly 97)

Exploration (RL, Millan 97) Foraging (GA, Jefferson 93)

Area Integration (RL,Versino 97)Exploration (RL, Hayes 01)Nest-building (Bonabeau, GA 00)

Evolutionary Encoding of theDistributed Nest Building Problem

• Phenotype: agent endowed with set of microrules• Genotype: set of microrules (one-to-one mapping with phenotype);

chromosome of variable length; 1 gene = 1 microrule.• Life span: number of iteration (e.g. 30,000 iterations, 1 iteration = all 10

agents have applied their microrule) or exhausting of max amount of bricksavailable (e.g. 500 bricks).

• Population: 80 individuals• Generations: 50• Fitness function: weighted sum of space filling and pattern replication

(arbitrary criteria based on 17 human observers who were asked to evaluatethe amount of structure in a set of 29 different patterns, [Bonabeau 2000]);

• Selection: roulette wheel• Crossover: two-points, pcrossover = 0.2• Mutation: pmutation1 = 0.9 (during life span inactive microrule);

pmutation2 = 0.01 (during life span active microrule).

Figure 6.12

Swarm Intelligence: Bonabeau et al

Evolutionary Encoding of theDistributed Nest Building Problem

• Biased evolution:• Probabilistic templates (reduction of stimulating configurations

containing a large number of bricks).• Start microrule.• No diagonal deposits (space not filled).

• Problems:• Fitness function: Functional? Esthetical? Biological plausible?

Mathematical definition?• Episthatic interactions (sequence of microrules needed for

coordinated algorithms).

twhiteStarting population is biased too: mean 50, std dev. 10

twhiteReally nice thesis on applying GP to this problem: linear representation is at fault here.

Collective Building andSelf-Assembly in Artificial

Systems

Applications• Obstacle avoidance in highly constrained and

unstructured environments• Formation of bridges, buttresses, stairs and

other structures for emergencies• Envelopment of objects

– Recovering satellites from space• Inspections in constrained environments

– Nuclear reactors• Self-organizing unfolding structures

– Space stations– Satellites– Scaffolds

Passive Bricks

Passive Bricks

• In-line 2D structures using Kheperas [Martinoli99] -> [Easton ??]

• Lionel Penrose (1898 – 1972)• Self-assembly mechanisms of genetic relevant

molecules reproduced with passive wood bricks• External energy source (shaking, human action)• Evolution and self-assembly tightly coupled• Video-tape!

twhiteTitle: Mechanical self-replication. Author(s): Lionel S. Penrose and Roger Penrose. Year(s): 1959. Model: Simple mechanical units. Implementation: Plywood. Reference(s): L. S. Penrose. ``Self-reproducing machines.'' Scientific American, Vol. 200, No. 6., pages 105-114, June 1959. Description: Lionel Penrose, aided by his son Roger Penrose, built simple mechanical units or bricks. An ensemble of such units were placed in a box, which was then shaken. As described by Penrose (1959): ``In fanciful terms, we visualized the process of mechanical self-replication proceeding somewhat as follows: Suppose we have a sack or some other container full of units jostling one another as the sack is shaken and distorted in all manner of ways. In spite of this, the units remain detached from one another. Then we put into the sack a prearranged connected structure made from units exactly similar to those already within the sack... Now we agitate the sack again in the same random and vigorous manner, with the seed structure jostling about among the neutral units. This time we find that replicas of the seed structure have been assembled from the formerly neutral or ``lifeless'' material.''

Passive BricksEvolutionary architecture: Nicolas Reeves (UQAM Montreal, Canada)• Cellular automata approach.• No genetic operator: population manager replaced by direct intervention of the

human being.

3D CAD simulations Stereolytographic sculptures

twhiteStrong resemblance to L-systems

Passive BricksEvolutionary architecture: Pablo Funes (Brandeis University, US)• GA approach, geometry- and force-based fintness functions• Off-board evolution and reproduction of results with Lego® bricks• 2D (bridge, crane) and 3D structures (table)

Set-up and objective Diagram of forces

Passive BricksEvolutionary architecture: Pablo Funes (Brandeis University, US)

2D Ex.: Bridge

Simulated bridge Real Lego bridge

twhite

Passive BricksEvolutionary architecture: Pablo Funes (Brandeis University, US)

3D Ex.: Table2D Ex.: Crane

Active Mechatronic Units

Research on Self-ReconfigurableMechatronic Systems in MEL

Eiichi Yoshida Satoshi MurataHaruhisa Kurokawa Kohji Tomita

Shigeru Kokajihttp://www.mel.go.jp/

MELMechanicalEngineeringLaboratory

twhiteMechatronic robots: independently controlled mechatronic units. Units have memory, CPU, power etc.

Can connect, disconnect and communicate with their connected units.

• Distributed mechanical system• composed of many identical units• using local communication only• dynamically reconfigurable

• Self-assembly / Self-repair

arbitrary initial state self-assembly

self-repairusing spare unitstrouble cut off


Self-ReconfigurableMechatronic Systems

• Self-assembly and Self-repair

0.01.02.03.04.05.06.0

0 100 200 300 Step

differencediffusion var.

diffe

renc

e / d

iffus

ion

var.

per u

nits

remove


Self-ReconfigurableMechatronic Systems

Weight: 50[g] (approx. )Span: 50[mm]

SMA TorsionCoil Springs

Male:Rotating Drum

Female:Auto-locking

(releasing by SMA)

SMACoil Spring

Pin holespin

VerticalDirection

Basic Motion


2D Self-Reconfigurable Systems

• Experiment using 6 units

Initial Final

Moving Unit

MELMechanicalEngineeringLaboratory 2D Self-Reconfigurable Systems

twhiteVERY difficult planning problem -- exponential growth of state space with number of independent units.

• Basic Motion

A


twhiteYoshida: moves in vertical plane.2 retractable arms, connect with a lock and key mechanism.

Used to create stairs, bridges ...


Grey: distance from target different from zeo -> to be movedGreen: distance from target = 0 -> ok!Red: current moved unit (once at the time)

• 3D Mechanical Unit

connecting hand rotating arm span: 27.5cmweight: 6.8kgDC motor: 7W


twhitehttp://www-2.cs.cmu.edu/~unsal/publications/dars00_unsal_camera.pdf

Nice paper on the planning problem.

Modular Robotics (PolyPod)

Mark Yim (Stanford University, Xerox Park Research Center, 1994- …)

Parallel structure, 10 links Two types of modules: segment andnodes

• Nodes: power modules• Segments: HC11 microcontroller,

angle position (potentiometer), IR localcommunication

Modular Robotics (PolyPod)


twhiteRolling ...

twhiteWalking ....

twhite

Modular Robotics (PolyBot)


This generation of PolyBot included onboard computing(Power PC 555) as well as the ability to reconfigureautomatically via shape memory alloy (SMA) actuatedlatches.

The future … PolyBot

twhiteMuscle Wires and SMAsShape Memory Alloys (SMAs) are unique composites that undergo a crystaline phase change (shape change) when heated or cooled. Nitinol, a SMA combination of nickel and titanium, can be processed into various forms and is an alternative for obtaining motion

NanoMuscle™The amazing new NanoMuscle Actuator contains a long length of SMA Wire in a compact electromechanical device that amplifies the stroke and motion of the wire, and provides for easy mounting and controllability. For silent, direct linear action, grab hold of NanoMuscles.

Muscle Wires & SMAsShape Memory Alloys (SMAs) are unique composites that undergoe a crystaline phase change (shape change) when heated or cooled. Nitinol, a SMA combination of nickel and titanium, can be processed into various forms and is an alternative for obtaining motion from electrical current. Typically motors or solenoids would be used, but there are many cases when SMAs are advantageous because of their mechanical simplicity, high strength to weight ratio, silentness, and precise control.

Self-Configurable and Modular Robotics

Other researchers:• Hajime Asama, RIKEN Center, Saitama, Japan

• http://www.riken.go.jp/eng/index.html• Distributed assembling and disassembling of stair-like

structures

• Peter Will and Wei-Min Shen, ISI-USC, L.A.,US• http://www.isi.edu/conro• Modular robotics

twhiteRun video of snake-like mechatronic robot rising and others.

twhiteDescription of the CONRO system.

Crystalline Atomic Modular Self-reconfigurable Robot

• http://www.ai.mit.edu/~vona/xtal• Autonomous self-reconfiguring robots• Simulator (Xtalsim) created

– 2D and 3D• Automated planning algorithm developed

– Melt-Grow planner: O(n2) for n atoms

Conclusion

Self-Assembly, Distributed Building, Modular Robotics

Very promising domain but

• System design and hardware development are crucial

• Energetic problem still unsolved

• Scalability and distributed control still unsolvedproblems: how to control individual units forobtaining a given team performance?

Evolution and bio-inspiration can help:incremental/modular evolution, reverse engineering

Documents

and Artificial Systems Self-Assembly in Natural Collective Building andarpwhite/courses/5002/lectures/self... · 2002. 4. 1. · heterogeneities. • The social insect activity only