Self-Reconfigurable Robot - A Platform of Evolutionary Robotics

Self-Reconfigurable Robot- A Platform of Evolutionary Robotics

Satoshi MurataTokyo Institute of Technology / AIST

murata@dis.titech.ac.jp

Keynote SpeechAlife9Sept. 14, 2004Boston

Outline

IntroductionSelf-reconfigurable systemsModular transformer (M-TRAN)Demonstration of M-TRAN

Introduction

Hierarchy in biological systemHomo/heterogeneous layers alternately appear in biological system (Masami Ito)

Species

Individual

Organ

Cell

Organelle

Molecule

homo

homo

homo

hetero

hetero

hetero

Heterogeneous systems

Made of heterogeneous componentsCentralizedSequentialGlobal interaction

Design principle --- Reductionism

Homogeneous systemsMade of homogeneous components

Distributed ParallelLocal Interaction

Design principle--- Self-organization

Advantages of homogeneityScalability

Enlarge / reduce system size in operation

RedundancyFault toleranceSelf-repair

FlexibilitySelf-assemblySelf-reconfiguration

Self-assembly in different scales

Molecular self-assemblyProteins, DNA tiles, etc.

Mesoscopic self-assemblyParticles, bubbles, E-coli, etc.

Robotic self-assemblyModular robotsModular robotsMobile agents

Small, simple,a large number of elements, difficult to control

Large, complicated, a small number of elements,programmableprogrammable

Self-reconfigurable systems

Self-reconfigurable systems

Artifacts based on homogenous modular architectureChange their shape and function according to the environment

(Self-reconfiguration)

Able to assemble itself, and repair itself without external help

(Self-Assembly, Self-Repair)

Homogeneous modular architecture

The system made of many (mechanical) modulesEach module is identical in hardware and softwareEach module has computational and communication capabilityEach module can change local connectivity

Self-assembly and self-repair

Random shape Assemble target shape

Detect failure Cutting off Reassemble

2-D Regular Tessellations

2-D Self-reconfigurable hardware

Micro-module (MEL, 98) Metamorphic robot (G.Chirikjian, JHU,93)

2-D Crystaline (M.Vona, D.Rus, Dartmouth Col./MIT)

Fracta (Murata, 93)Solid state module based on hexagonal lattice

Basic operations of fracta

Self-assembly problem

How to change connectivity among modules to achieve target configuration ?

You must consider• Modules are homogeneous• Parallel and distributed• Only local communication• Physical constraints

Random

Given

Example: Self-assembly of fracta

Parallel algorithm based on connection types and local communication

Connection types Target shape

o(K,K)K(o,K,K,s)s(K,K,K,K,K,K)

Exchange connection type with neighbors

Program code

Local configurations

Parallel distributed algorithm for self-assembly

1. Each module evaluates distance to the nearest target configuration in the program code

2. Modules compare the evaluation through simulated diffusion

3. Module which wins among the neighbors moves to random direction

Type transition diagram defines metric among connection types

Difficulties in 3-D hardware

More mobility in limited spaceSpatial symmetry requires more degrees of freedom More power/weight Mechanical stiffness

Space filling polyhedra

Rhombic dodecahedron

Truncated octahedron

Regular cube

Lattice based designs

3-D Crystaline(M. Vona, D.Rus，Dartmouth, MIT)

Design based on cube Design based on rhombic dodecahedron

Proteo （M.Yim, PARC, 2000）

Design based on cube

3-D Universal Structure (MEL, 98)

Lattice based designs

Molecule （Kotay, Rus, Dartmouth/MIT）

Chain based designs

PolyBot: M.Yim ,Xerox PARC

CONRO: W-M.Shen, P.Will, USC

Lattice or chain ?Lattice based designs

Reconfiguration is easyMotion generation is hardRequires many connectors & actuators

Chain based designsReconfiguration is hardMotion generation is easyInsufficient stiffness

M-TRAN (Modular Transformer)

M-TRAN（Modular Transformer）

Hybrid of lattice and chain based designs

Easy self-reconfiguration and robotic motionTwo actuatorsCommunicationStackableBattery driven

M-TRAN II

M-TRAN Module

Li-Ion battery

Power supplycircuit

Acceleration sensor

Neuron chipPIC

Main CPU Connecting plate

Permanent magnet

SMA coil

Non-linear spring

Light bulb

PIC

M-TRAN II

M-TRAN I

Magnetic connection mechanism

Distance(mm)

Forc

e (a)

(b)(c)

Temperature (ºC)

Forc

e

0 10 20 30 40 50 60 70 80 90 100

Distance

Non-linearspring

SMAcoil

Light bulbMagnet

Attraction by magnets

Repulsion by springsDetach

SMA Actuator

a - b

New prototype

M-TRAN III Hook connection mechanism• Quick• Reliable

Coping with complexityBecause of physical constraints such as

Maintain connectivityAvoid collisionLimited torqueNon-isotropic geometry of M-TRAN module

makes self-reconfiguration very difficult

Complexity can be relaxed byAutomatic acquisition of rule setHeuristics (structured rule set)Periodical pattern in structure

Wall climbing

600 rules (no internal state)Generated by software

18 rules (with internal state)Hand-coded

Creeping carpet

Robot maker (structured rule set)

Central Pattern Generator (CPG)Connected neural oscillatorsOscillators entrain phases mutually Feedback of physical interaction

Rhythmic motion generation

CPG

Neural connection (CPG network)

Motor control Angle feedback

Mechanical interaction

β

τ τ’

Extensor Neuron

β

τ τ’

Flexor Neuron

),0max( 11 ii uy =

),0max( 22 ii uy =

m1

ue

w0

ue

f1i

f2i

i–

+

Extensor

Flexorm2

y1i

y2i

Σ

Σ

CPG

Output to motor

u1i v1i

u2i v2i

Input toOther CPGs

Output from other CPGs

Output from other CPGs

Joint angle feedback

Joint angle feedback

CPGAntagonistically connected pair of

nonlinear oscillators

(Taga 95, Kimura 99)

CPG network

x

z

y

CPG

Excitatory connection

Inhibitory connection

Generate stable walk pattern (limit cycle)

CPG network tuned by GA

GA optimizesConnection matrix of CPGJoint angles in initial posture

by evaluating Energy consumptionper traveled distance

Given topology of robot

Initial set of individuals

Dynamics Simulation

Mutation, crossoverSelection

Download to modules

Yes

Generation +1

Simulation space

Converge？

Dynamics Simulation

Before GA After GA

Vortex simulator (CML)

Obtained CPG network for 4-leg walker

+1-1

-3

-2

-1

0

1

2

3

1

21

41

61

81

101

121

141

161

181

201

221

241

261

281

301

321

341

361

381

401

421

441

461

481

501

521

541

561

581

601

621

641

661

681

Symmetric connection is obtained

Forward

Real-time morphology controlAdapt morphology suitable to the environment

Rapidly-Exploring Random Trees (RRTs)

Self-reconfigurable robots~ A new kind of artifacts

Locomotive flow of periodic cluster

Morphing

Reconnection to cluster

Swarm

Individual

Amoeba

Producing individual agents

Conclusion

Self-reconfigurable systems give a platform upon which we can investigate both individual adaptation and morphological evolution concurrently in a single framework.

In this sense, self-reconfigurable systems open the new possibility of artifacts beyond natural evolution.