SELF-RECONFIGURABLE ROBOTS

Self-Reconfigurable Robot (A Platform of Evolutionary Robotics)

O P GujelaVilnius Gediminas Technical University, Lithuania

opgujela@gmail.com

Outline

• Self-reconfigurable systems• Modular transformer (M-TRAN)• Demonstration of M-TRAN

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)

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

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

Solid state module based on hexagonal lattice

Basic operations of fracta

Self-assembly problemHow to change connectivity among modules to achieve target configuration ?

Point to be Consider• Modules are homogeneous• Parallel and distributed• Only local communication• Physical constraints

Random

Given

11

Example: Self-assembly of fracta

Parallel algorithm based on connection types and local communication

Connection types

Target shape

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

Parallel Distributed Self-Assembly

Type transition diagram

Difficulties in 3-D hardware

More mobility in limited space• Spatial 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

Molecule（ Kotay, Rus, Dartmouth/MIT）

3-D Universal Structure (MEL, 98)Lattice based designs

Chain based designs

PolyBot: M.Yim ,Xerox PARC

CONRO: W-M.Shen,

P.Will, USC

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

M-TRAN （Modular Transformer）

Hybrid of lattice and chain based designs

• Easy self-reconfiguration and robotic motion

• Two actuators

• Communication

• Stackable

• Battery driven

M-TRAN II

M-TRAN Module

Li-Ion battery

Power supply circuit

SMA coil Acceleration sensor

M-TRAN II

Neuron chipPIC

Main CPU Connecting plate

Permanent magnet

Non-linear spring

Light bulb

PIC

M-TRAN I

New prototype

M-TRAN III Hook connection mechanism• Quick• Reliable

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

Wall climbing

600 rules (no internal state)

Generated by software18 rules (with internal state)

Hand-coded

Creeping carpet

Robot maker (structured rule set)

• Central Pattern Generator (CPG)• Connected neural oscillators• Oscillators entrain phases mutually• Feedback of physical interaction

Rhythmic motion generation

CPG

Neural connection (CPG network)

Motor control Angle feedback

Mechanical interaction

CPG network

x

CPG

z

y

Excitatory connection

Inhibitory connection

Generate stable walk pattern (limit cycle)

CPG network tuned by GA

GA optimizes• Connection matrix of

CPG• Joint angles in initial

posture

by evaluating• Energy consumption

per traveled distance

Simulation space

Given topology of robot

Initial set of individuals

Dynamics Simulation

Mutation, crossover Selection

Download to modules

Yes

Generation +1

Converge？

Thanks For Your Kind Attention