Self-Reconfigurable Robot- A Platform of Evolutionary Robotics
Satoshi MurataTokyo Institute of Technology / AIST
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.
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