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Multiagent control of Self-reconfigurable Robots -Hristo bojinov,Arancha Casal,Tad Hogg
Harini Gurusamy
Features
Identical simple Modules Dynamic adaptation Multiagent Control De-Centralized Control No complex sensors
Real time e.g
Ant Colony
Amoeba
Two approaches
o Combinatorial Searcho Identify motion of modules 1.Module Specificationo Not suitable to real timeo 2.Agent based Controlo Creating structures with propertieso Decomposes control problems
Assumptions
Limited computational capabilities Limited Memory Simple FSM No global broadcast Limited communication
Experimental Robot PlatformProteo-Modular self reconfigurable robot
Polypod
Features
Substrate reconfiguration Geometric constraintso Dodecahedrao Max Internal volumeo Rotations-120 Complexity-12 face-Actuation Direct Communication
Simulations
Asynchronous operation Behaviour execution Different random order Denial of movement Notification to control programs No power limits
How a module samples from the availabe moves??
Equal probability Directed random move Positive dot product E.g.direction of ball
Control Primitives
Growth Seed Scents Mode-present FSM
Modules-search,seed,Final,Node
Recursive branching for Locomotion & Manipulation
SLEEP SEARCH SEED FINAL NODE-spawns seeds INODE-emit node scent & propagates
regular scent
Dynamic adaptation to external forces
Supporting weight on legs Neglect weights of modules 1 level and 2 level branching Fixed Module Transmit scentRoot Module IROOT-Avg wt > Fmax with Pmax AROOT ROOT-Avg wt < Fmin with Pmin
Contd..
Probabilistic approach Avoids oscillations Time order -200 cycles Probability to change state-1/R Impact on real world?????????
Grasping objects
SLEEP SEARCH SEED TOUCH & TOUCHSEED FINAL
Local minima & Stability
Physical motion Constraints Surface scent Alter the design Careful reconfiguration rules
Conclusion
Local simple rules Genetic algorithms & FPGA Protein motors
The USC/ISI CONRO Project
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