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Self-Reconfigurable Robot - A Platform of Evolutionary Robotics
1. Keynote Speech
Alife9
Sept. 14, 2004
Boston
Self-Reconfigurable Robot
- A Platform of Evolutionary Robotics
Satoshi Murata
Tokyo Institute of Technology / AIST
murata@dis.titech.ac.jp
2. Outline
Introduction
Self-reconfigurable systems
Modular transformer (M-TRAN)
Demonstration of M-TRAN
4. Hierarchy in biological system
Homo/heterogeneous layers alternately
appear in biological system (Masami Ito)
Species hetero
Individual homo
Organ hetero
Cell homo
Organelle hetero
Molecule homo
5. Heterogeneous systems
Made of heterogeneous components
Centralized
Sequential
Global interaction
Design principle
--- Reductionism
6. Homogeneous systems
Made of homogeneous components
Distributed
Parallel
Local Interaction
Design principle
--- Self-organization
7. Advantages of homogeneity
Scalability
Enlarge / reduce system size in operation
Redundancy
Fault tolerance
Self-repair
Flexibility
Self-assembly
Self-reconfiguration
8. Self-assembly in different scales
Molecular self-assembly Small, simple,
Proteins, DNA tiles, etc. a large number of elements,
difficult to control
Mesoscopic self-assembly
Particles, bubbles, E-coli, etc.
Robotic self-assembly Large, complicated,
Modular robots a small number of elements,
Mobile agents programmable
10. Self-reconfigurable systems
Artifacts based on homogenous modular
architecture
Change their shape and function according
to the environment
(Self-reconfiguration)
Able to assemble itself, and repair itself
without external help
(Self-Assembly, Self-Repair)
11. Homogeneous modular
architecture
The system made of many (mechanical)
modules
Each module is identical in hardware and
software
Each module has computational and
communication capability
Each module can change local connectivity
18. Self-assembly problem
How to change
connectivity among
modules to achieve target
configuration ?
Random
You must consider
• Modules are homogeneous
• Parallel and distributed
• Only local communication
• Physical constraints
Given
19. Example: Self-assembly of fracta
Parallel algorithm based on connection
types and local communication
Connection types Target shape
21. 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 Type transition diagram
direction defines metric among
connection types
22. Difficulties in 3-D hardware
More mobility in limited space
Spatial symmetry requires more degrees
of freedom
More power/weight
Mechanical stiffness
24. Lattice based designs
Design based on cube Design based on
rhombic dodecahedron
3-D Crystaline
(M. Vona, D.Rus，Dartmouth, MIT)
Proteo （M.Yim, PARC, 2000）
25. Lattice based designs
Design based on cube
Molecule
（Kotay, Rus, Dartmouth/MIT）
3-D Universal Structure (MEL, 98)
27. Lattice or chain ?
Lattice based designs
Reconfiguration is easy
Motion generation is hard
Requires many connectors & actuators
Chain based designs
Reconfiguration is hard
Motion generation is easy
Insufficient stiffness
29. M-TRAN（Modular Transformer）
Hybrid of lattice and chain based designs
Easy self-reconfiguration and robotic motion
Two actuators
Communication
Stackable
Battery driven
32. Li-Ion battery Non-linear spring
Light bulb
PIC
Connecting plate
Main CPU
Power supply
circuit Permanent magnet
PIC Neuron chip
SMA coil
Acceleration sensor
M-TRAN II
34. Magnetic connection mechanism
Magnet
Light bulb
Distance
SMAcoil Non -linear
spring
SMA Actuator
Force
(a)
Attraction by magnets
Force
Repulsion by springs
Detach (b)
(c)
0 10 20 30 40 50 60 70 80 90 100
a-b
Temperature (ºC)
Distance(mm)
35. New prototype
M-TRAN III Hook connection mechanism
• Quick
• Reliable
36. Coping with complexity
Because of physical constraints such as
Maintain connectivity
Avoid collision
Limited torque
Non-isotropic geometry of M-TRAN module
makes self-reconfiguration very difficult
Complexity can be relaxed by
Automatic acquisition of rule set
Heuristics (structured rule set)
Periodical pattern in structure
37. Wall climbing
600 rules (no internal state) 18 rules (with internal state)
Generated by software Hand-coded
40. Rhythmic motion generation
Central Pattern Generator (CPG)
Connected neural oscillators
Oscillators entrain phases mutually
Feedback of physical interaction
Mechanical interaction
Motor control Angle feedback
CPG
Neural connection (CPG network)
41. CPG
Antagonistically connected pair of
nonlinear oscillators
Output from other CPGs CPG
y1i
Extensor Neuron
ue
τ τ’
Σ u1i β v1i
Joint angle feedback f1i Extensor
m1 y1i = max(0, u1i )
– Input to
Output to motor
i w0
+ m2 y2 i = max(0, u2 i )
Other CPGs
f2i Flexor
Joint angle feedback Σ u2i β v2i
y2i
τ τ’
ue
Flexor Neuron
Output from other CPGs
(Taga 95, Kimura 99)
42. CPG network
Generate stable walk pattern (limit cycle)
y
Inhibitory
Excitatory z connection
connection
x
CPG
43. CPG network tuned by GA
Simulation space
GA optimizes
Given topology of robot
Connection matrix of
CPG
Initial set of individuals
Joint angles in initial
posture
Converge？
Dynamics Simulation
by evaluating Yes
Generation +1
Energy consumption
Mutation, crossover
per traveled distance Selection
Download to modules
48. Self-reconfigurable robots
~ A new kind of artifacts
Amoeba
Reconnection to cluster Locomotive flow of periodic cluster
Individual
Producing individual agents
Morphing
Swarm
49. 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.
