Self-replicating robots for lunar development - Mechatronics, IEEE
SELF-RECONFIGURABLE ROBOTS
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Transcript of SELF-RECONFIGURABLE ROBOTS
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Self-Reconfigurable Robot (A Platform of Evolutionary Robotics)
O P GujelaVilnius Gediminas Technical University, Lithuania
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Outline
• Self-reconfigurable systems• Modular transformer (M-TRAN)• Demonstration of M-TRAN
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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)
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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
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Self-assembly and self-repair
Random shape Assemble target shape
Detect failure Cutting off Reassemble
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2-D Regular Tessellations
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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)
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Fracta
Solid state module based on hexagonal lattice
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Basic operations of fracta
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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
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11
Example: Self-assembly of fracta
Parallel algorithm based on connection types and local communication
Connection types
Target shape
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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
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Difficulties in 3-D hardware
More mobility in limited space• Spatial symmetry requires more degrees
of freedom• More power/weight• Mechanical stiffness
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Space filling polyhedra
Rhombic dodecahedron
Truncated octahedron
Regular cube
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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)
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Design based on cube
Molecule( Kotay, Rus, Dartmouth/MIT)
3-D Universal Structure (MEL, 98)Lattice based designs
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Chain based designs
PolyBot: M.Yim ,Xerox PARC
CONRO: W-M.Shen,
P.Will, USC
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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
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M-TRAN (Modular Transformer)
Hybrid of lattice and chain based designs
• Easy self-reconfiguration and robotic motion
• Two actuators
• Communication
• Stackable
• Battery driven
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M-TRAN II
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M-TRAN Module
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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
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M-TRAN I
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New prototype
M-TRAN III Hook connection mechanism• Quick• Reliable
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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
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Wall climbing
600 rules (no internal state)
Generated by software18 rules (with internal state)
Hand-coded
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Creeping carpet
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Robot maker (structured rule set)
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• 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
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CPG network
x
CPG
z
y
Excitatory connection
Inhibitory connection
Generate stable walk pattern (limit cycle)
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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?
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Thanks For Your Kind Attention