Distributed Intelligent Systems – W13:Self-Aggregation and Self-Assembly in Natural and Assembly in Natural and

Artificial Systemsy

1

OutlineOutline• Self-aggregation in artificial

systems• Collective decision mediatedCollective decision mediated

by self-aggregation (mixed societies)societies)

• Self-assembly General background– General background

– Artificial examples (sub-mm and cm scales)and cm scales)

2

S lf A ti i Self-Aggregation in Artificial Systemst c a Syste s

3

Robot Self-Aggregation using Wireless Communication - Overview

[Correll & Martinoli, IJRR 2011][ , ]

• Eg. 2 clusters of 3 robots, 6 of 1 robot• Webots experiments using Alice II with

radio, 12 robots totalA di t 1

Real robotic node: Alice II with 802.15.4 com module

• Arena diameter: 1 m• Communication range: 7-12 cm• Simplified com model 4

Original Inspiration: Aggregation in Gregarious Arthropods

• Aggregation in Cockroaches• Individual Behavior is well

di dstudied• Probability to rest is a

function of the number offunction of the number of cockroaches nearby

• Probability to move ydecreases when in larger aggregates

© Sempo, Halloy, Detrain & Deneubourg

5

Individual Behavioral Parameters

pjoin pleave

D ll d bData collected by Jeanson et al. (Animal Behavior 2004)

# of cockroaches nearby

6

Experimental Setup• Submicroscopic model (Webots)• Estimate of neighborhood using local

communication used as ancommunication used as an “omnidirectional teammate sensor”

• Local communication: – presence or absence of a teammate in thepresence or absence of a teammate in the

communication range– use of static or from a large set dynamically

randomly chosen IDs for facilitation of the discriminationdiscrimination

– no additional explicit information shared (e.g., counting process involving multi-hop, etc.)

• Objective: relation between individual and collective dynamics rather than biological accuracy

i i ( h d f• Systematic experiments (thousands of runs) infeasible with biological agents

7

Individual Robot BehaviorIndividual Robot Behavior

pjoin(Environment)

Search Rest

pleave(Environment)

Note: avoidance behavior for walls implemented but neither represented here nor captured by the models (impact negligible) 8

Microscopic and Macroscopic Modeling

9

AssumptionsAssumptions• A robot moves through the environmentA robot moves through the environment

(random walk) during which it encounters other robots with constant probabilityp y

• The probability to encounter one robot is pc, the probability to encounter and join a cluster p y jof n robots pjoin(n) ; the probability of leaving such cluster pleave(n)

Uniform distribution of objects in the environment and linear super-position of p pencountering probabilities

10

Probabilistic Finite State Machinef I di id l R bfor an Individual Robot

Robot joining/leaving aggregatesgg g

“Passive” state transitions

b bilit ti t t t th b t

transitions

pc = probability per time step to encounter another robotNx = fraction of time spent in state x 11

“Passive” State TransitionsPassive State Transitions

j j 1j j+1

Example: 4 robots change their state without actually moving

12

Probabilistic Finite State Machine f th Wh l S tfor the Whole System

Ns= average number of robots in searchNj= average number of robots in an aggregate of size j 13

Probabilistic Finite State Machine

14

Number of Agents in a j-size Cluster

• Flows from a single aggregate

jpcpjoin(j) jpcpjoin(j-1)

j-Aggregate

jpleave(j) jpleave(j+1)jpleave(j) jpleave(j+1)

15

Average Number of Agents in a j-size Cluster

j iNs(k)Nj(k)jpcpjoin(j) Ns(k)Nj-1(k) jpcpjoin(j-1)

j-Aggregate

Nj(k)jpleave(j)Nj+1(k)jpleave(j+1)

16

Sample Results and Sample Results and Discussion

17

Temporal Evolution of the Self-A i P (12 b )Aggregation Process (12 robots)

Submicroscopic model (Webots)• 1500 runs

Macroscopic model

• 3 h (10800 s) per run • sample time 10 s•10 cm communication range 18

Temporal evolution of the SelfTemporal evolution of the Self-Aggregation Process (1 run)

19

Modulating the Encountering ProbabilityModulating the Encountering Probability by the Communication Range (12 robots)

• 7cm communication range

• 1500 runs• 12 cm communication range

• 1500 runs 20

DiscussionDiscussion

• The “encountering probability” is indeed a function of the communication range, which leads to a bifurcation of the system dynamics (scattered vs. giant component)

• Aggregation performance of the biological algorithm very low

• Might be the sole choice for micro-robots

21

DiscussionDiscussion

• Modeling limitations:– Clusters are assumed to be circular– Only one individual joins/leaves at a time– Clusters do not split/merge

• Function of robot shape/sensorial characteristics

22

S lf A ti i Mi d Self-Aggregation in Mixed Natural-Artificial Systemsy

23

The LEURRE Project

• Leurre: European project focusing on mixed insect-robot

The LEURRE Project

Leurre: European project focusing on mixed insect robot societies (http://leurre.ulb.ac.be)

• Relevant Leurre partners for the presented work:

• ULB (coordinator modeling experimentalULB (coordinator, modeling, experimental work with insects/robots)

• Uni Rennes/CNRS (chemistry)EPFL ASL/LSRO ( bil b t d i• EPFL – ASL/LSRO (mobile robots design and development)

• EPFL – DISAL (vision-based tracking and d lli )modelling)

24

Collectively Selecting a ShelterCollectively Selecting a ShelterGoal of the project: quantitatively characterize the self-organized p j q y gcollective decision-making process of a cockroach society by unravelling and influencing the local interaction rules

• A simple decision-making scenario: 1 arena, 2 shelters

• Shelters of the same and different darkness

• Groups of pure cockroaches (16)Groups of pure cockroaches (16), mixed robot+cockroaches (12+4)

• Infiltration using chemical

[Halloy et al., Science, Nov. 2007]

camouflage and statistical behavioral model 25

Sample ResultsSample ResultsLegend: mixed, pure; shelter 1, shelter 2

A . Symmetric shelters (infiltration) B. Asymmetric shelters (active control)Experiment

Macroscopic Model

Experiment

Macroscopic Model

26

The EPFL Tool Contributions3. Multi-level modeling

( 1) ( ) ( ) ( 1) ( ) ( ) ( )join joinN k N k p N k j p j N k p j N k Macroscopic

1. Robots as a flexible, interactive, societal “microscope”

Ss Sa

1

1

( 1) ( ) ( ) ( 1) ( ) ( ) ( )

( ) ( ) ( 1) ( )j j r s j j

leave leavej j

N k N k p N k j p j N k p j N k

p j N k j p j N k j

Agent-based microscopic

Macroscopic model

Ss SaS S

Module-based

pmodel

Ss Sa

[Asadpour et al., ARS J., 2006]

microscopic model2. A marker-less vision-based tracking

tool: SwisTrack

Target system

[Correll et al., ALife J., in prep.][Correll et al., IROS 2006, Lochmatter et al., IROS 2008]; http://sourceforge.net/projects/swistrack 27

Self-Assembly in Natural and Artificial Systems and Artificial Systems

28

Wh t i S lf A bl ?What is Self-Assembly?Spontaneous organization of pre-existing units

into spatial structures or patterns without external guidance.

Self-Assembly at

29 orders of magnitude

Self-Assembly at All Scales

29 orders of magnitude, same underlying

mechanism!

Our galaxy, the Milky Way, about 1021 m

Carbon nanotube, about 10-8 m 29

Wh t i NOT S lf A bl ?What is NOT Self-Assembly?Planned and externally

Living beings, because they are not made of

guided constructions pre-existing unitsUnstructured aggregates

Gazprom building, St-Petersburg, 102 m

Boxelder bug aggregate, 10-3 m

Various animals

Sequential, limited scalability (from a few

illi t t f

Aggregation may serve a purpose (e.g., protection,

ll ti d i i

Self-assembly may have played a key role in the

i iti l f lifmillimeters up to a few kilometers)

collective decision-making), but the structure

itself does not.

initial appearance of life.

30

Self-Assembly at All Scales

D SA f 1 i d h l l t i

A.Crystal structure of a ribosome [Yusupov et al 2001]

D. SA of 1-mm-sized hexagonal plates using “capillary bond” [Whitesides, Harvard]

E. SA of electrical networks [Whitesides, Harvard]al., 2001]

B.Scaffolded DNA origami of 100 nm in diameter [Rothemund, Caltech]

C.Capillary SA of 10-μm-sized hexagonal

]F. Electromechanical programmable parts

specifically designed for SA [Klavins, University of Washington]

G A swarm of mobile robots capable of SAplates into large crystal structures [Whitesides, Harvard]

G. A swarm of mobile robots capable of SA [Dorigo, UniversitéLibre de Bruxelles]

31

Ingredients and Features Ingredients of Self-Assembly:

1. Positive feedback: binding force, structural reinforcement2 N ti f db k l i f ( th i l2. Negative feedback: repulsive force (otherwise, merely

aggregation)3. Multiple reversible interactions4. Randomness

Features of Self-Assembly: Robustness: self-assembly deals with component defects,

environmental noise and external perturbations Parallelism: self-assembly occurs wherever there are interacting y g

components Scalability: self-assembly is a strategy applicable at all scales,

and even across scales Ubiquity: structures form spontaneously at all scales in Nature,

and most of them exhibit similarities across scales 32

M ti ti f St d i S lf A bl (1)Motivation for Studying Self-Assembly (1)Scientific understanding of a broad variety of

phenomena:phenomena:

Water ripples~10-3 m

Seashell~10-2 m

School of fish~101m

DNA origami~10-7m

Tornado~102 m

Cyclone~106 m~10 7m ~102 m ~106 m

33

Motivation for Studying Self-Assembly (2)Motivation for Studying Self-Assembly (2)Manufacturing and coordination of ultra-small

distributed intelligent systems:distributed intelligent systems:

Left: A microrobot (10 μm) is inoculating a red blood cellagainst someLeft: A microrobot (10 μm) is inoculating a red blood cellagainst some kind of disease. Right: This nanoscaled device (1 nm) enables a nanorobot to catch HIV individuals and destroy them. 34

The quest for miniaturizationHow to go from the Alice robot (2cm in size)…

...to fully-fledged microrobots?35

Limitations of the Top-Down Approach

HOW TO ASSEMBLE

INTO THESE USING THESE?THESE

ANSWER: LET THEM ASSEMBLE THEMSELVES!36

Self-Assembly of Micro-Systems

bi di fBinding surface

Non-binding surface

Self-AssemblySelf-Assembly Hydrophobic interaction and

capillary forces (in fluid)

V d W l f (i d Van der Waals forces (in dry state)

Experimental challenges: imaging (3D high speedimaging (3D, high speed camera, microscope) and controllability

37

Intelligent… and less intelligent agentsAli bil b t MEMSAlice mobile robot MEMS

100µm2cm

Typical size: 2 centimeters Typical size: 50 to 500 μm

100µm2cm

Typical swarm size:< 100 units Sensing, computation,

communication (only local)

Typical swarm size:> 104 unitsNo sensing, computation or

communication, but localcommunication (only local)Controllable self-locomoted

units (but quite noisy)

communication, but local interactions only Stochastic motion only 38

Multi-Level Modeling MethodologyMacroscopic 1: rate equations mean field

stra

ctio

n

met

rics

Macroscopic 2: Chemical Reaction Network, t h ti i l ti

Macroscopic 1: rate equations, mean field approach, whole population

Microscopic 1: Monte Carlo model, 1 robot = 1 agent non-spatial

Abs

Com

mon

stochastic simulations

agent, non spatial

Microscopic 2: Agent-Based model, 1 robot = 1 agent spatial

ime

agent, spatial

Realistic: faithful representation of the robots (e g geometry S&A) and the environment (e g

rim

enta

l ti(e.g., geometry, S&A) and the environment (e.g.,

friction, gravity, inertia)

Physical reality: real experiments, imaging with overhead cameras, tracking software E

xpe

39

Multi-Level Modeling MethodologyMacroscopic 1: rate equations mean field

stra

ctio

n

met

rics

Macroscopic 2: Chemical Reaction Network, t h ti i l ti

Macroscopic 1: rate equations, mean field approach, whole population

Microscopic 1: Monte Carlo model, 1 robot = 1 agent non-spatial

Abs

Com

mon

stochastic simulations

agent, non spatial

Microscopic 2: Agent-Based model, 1 robot = 1 agent spatial

ime

agent, spatial

rim

enta

l ti

Underlying physical reality (and, therefore

submicroscopic models) may

Exp

esubmicroscopic models) may vary dramatically!

40

Ex.1: Self-Assembly of Alice RobotsG lGoal Formation of chains

Ph i l t tb d f d l t Physical testbed for models at multiple abstraction levels

Self assemblySelf-assembly “soft” binding force (implemented through local IR ( p gcommunication)

AdvantagesControllable, but still miniaturized 19 robots, accelerated 16x

Experimental ease: imaging, controllability, flexibility [Evans et al., ICRA 2010] 41

Single Robot AlgorithmTumble

Timer expiresTimer expires

OR

Start Wander

Unaligned object detected

Timer expiresAligned object

detected

AvoidBondNo recent communication

ORNo recently aligned object

Deterministic vs. stochastic variant42

Deterministic ControllerBonded Alices communicate with each other to determine the length of their chaindetermine the length of their chain

msg = 4

msg = msg + 1msg = 2

msg = 1

msg = 4

43

Deterministic Controller -Deterministic Controller Sample Results with 19 robots

Submicroscopic Macroscopic (Webots) (stochastic CRN)

44

Stochastic ControllerStochastic Controller

Every second, an Alice leaves its current bond depending on some

b bilitprobability.

45

Stochastic Controller - SampleStochastic Controller Sample Results with 19 robots

Submicroscopic MacroscopicSubmicroscopic (Webots)

Macroscopic (stochastic CRN)

46

Multi-Level ModelingMacro deterministic: rate equations

trac

tion

met

rics

Macro-stochastic: Chemical Reaction

Macro-deterministic: rate equations, mean field approach, whole population

Abs

tC

omm

on Network, stochastic simulations

Microscopic (non-spatial): Monte Carlo model 1 particle = 1 agent C

Microscopic (spatial): Agent-Based model 1 particle = 1 agent

model, 1 particle = 1 agentAUTOMATIONAUTOMATION

l tim

e

model, 1 particle = 1 agent

Underlying physical platforms

erim

enta

ly g p y pand submicroscopic models may

vary dramatically.

Exp

e

47

Alice Self-AssemblyReal System

(tracking)

M3 FrameworkM3 Framework

✗

1010 runs452 species

1474 reactions

[Mermoud, PhD EPFL 2012, STAR Volume Nr. 93] 48

Ex. 2: Self-Assembly of Lilies

40 runs6 species

12 reactions

mean

1.96·σ

[Di Mario et al., IROS 2011][Mermoud, PhD EPFL 2012]

49

Automated Model-Based Design

ModelingAUTOMATIONAUTOMATION

Model-basedDesign & Optimization

[Mermoud et al, ICRA 2012] 50

Model-Based Control of Self-Assembly Case study with four real Lily modules Two modes of agitation (bang-bang):

1 High agitation (exploration)1. High agitation (exploration)2. Low agitation (exploitation)

Simultaneous modeling & optimizationhapproach

Completely automated and real-time

Target 51

Qualitative ResultsQualitative Results

52

Quantitative results

ure

[s]

et st

ruct

uac

h ta

rge

me

to re

a

(low) (high)

Tim

40 runs of 30 minutes using 4 real Lily modules53

Ex 3 SelfSys: MEMS Self AssemblyEx. 3 SelfSys: MEMS Self-Assembly(a) (b)

100 µm

10001000 runs2 species

2 reactions

100 building blocks10 building blocks[Mermoud, PhD EPFL 2012][Jacot-Decombes et al, Soft Matter, 2013] 54

SelfSys: Experimental Set-Up

2 cm2 cm2 cm[Goldowsky et al., JMM 2013][Mastrangeli et al., ICRA 2014, submitted] 55

SelfSys: Building BlocksCylinders

y g

Tiles

200 m 200 m

56

AiSelfSys: Modeling and Agitation

Air socketsCFD simulations:

• Laminar flow (Re ~ 100)2 h fl ( t / i )• 2-phase flow (water/air)

• Acoustic actuation (45 KHz)

water5 mm

water

[Goldowsky et al., JMM 2013]57

SelfSys: Agitation Control Modes

10 Control space

y g

10 mm Control space

1st: 42 4 KHz

FEM Eigenfrequencies

1st: 42.4 KHz

[Goldowsky et al., JMM 2013] 58

SelfSys: Multi Modal Control of theSelfSys: Multi-Modal Control of the Agitation in the Microchamberg

[Goldowsky et al., JMM 2013] 59

SelfSys: TrackingTilesCylinders

200 m200 m200 m

[Mastrangeli et al., ICRA 2014, submitted] 60

Conclusion

61

Take Home MessagesTake Home Messages• Self-aggregation can be modeled with the usual multi-level

modeling techniques; the resulting models at the macroscopic g q g plevel are very similar to those of object aggregation

• Self-aggregation can facilitate collective decisions, especially in systems endowed with low-range communicationin systems endowed with low-range communication

• Self-assembly implies the constitution of structures and ordered spatial pattern (rather than just aggregate) and

i i l h i ll h i dconstituting elements that incorporate all the required assembly mechanisms (although they are not necessarily endowed with self-locomotion capabilities)

• Self-assembly can happen at all scales and is a powerful, parallel, potentially cheap coordination principle for a number of manufacturing applications in which traditionalnumber of manufacturing applications in which traditional techniques cannot be used or have an excessive cost

62

Additional Literature – Week 13Papers• S. Garnier, C. Jost, R. Jeanson, J. Gautrais, A. Grimal, M. Asadpour, G. Caprari,

and G. Theraulaz. “The Embodiment of Cockroach Aggregation Behavior in a Group of Micro-Robots”. Artificial Life, 14: 387-408, 2008.

• R. Gross, M. Bonani, F. Mondada, and M. Dorigo. Autonomous self-assembly in swarm-bots. IEEE Trans. on Robotics, 22(6):1115–1130, 2006.

• Gross R and Dorigo M "Self-assembly at the macroscopic scale " Proceedings of• Gross R. and Dorigo M. Self-assembly at the macroscopic scale, Proceedings of the IEEE, 96(9):1490-1508, 2008

• Goldowsky J., Mastrangeli M., Jacot-Descombes L., Gullo M. R., Mermoud G., Brugger J., Martinoli A., Nelson B. J., Knapp H. F., “Acousto-Fluidic System A i ti I Li id S lf A bl f Mi t ” J l fAssisting In-Liquid Self-Assembly of Microcomponents”. Journal of Micromechanics and Microengineering, 23: 125026 (11pp), 2013.

• Jacot-Descombes L., Martin-Olmos C., Gullo M. R., Cadarso V. J., Mermoud G., Villanueva L. G., Mastrangeli M., Martinoli A., and Brugger J., “Fluid-Mediated Parallel Self-Assembly of Polymeric Micro-Capsules for Liquid Encapsulation and Release”. Soft Matter, 9: 9931-9938, 2013.

• Mastrangeli M., Mermoud G., and Martinoli A., “Modeling Self-Assembly Across Scales: The Unifying Perspective of Smart Minimal Particles”. Special issue on y g p pSelf-Assembly, Böhringer K. F. and Elwenspoek M. editors, Micromachines, 2(2): 82-115, 2011.

• Klavins E., “Programmable self-assembly”. Control Systems, 27(4):43–56,2007. 63