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Multiagent control of Self-reconfigurable Robots -Hristo bojinov,Arancha Casal,Tad Hogg
Harini Gurusamy
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Features
Identical simple Modules Dynamic adaptation Multiagent Control De-Centralized Control No complex sensors
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Real time e.g
Ant Colony
Amoeba
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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
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Assumptions
Limited computational capabilities Limited Memory Simple FSM No global broadcast Limited communication
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Experimental Robot PlatformProteo-Modular self reconfigurable robot
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Polypod
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Features
Substrate reconfiguration Geometric constraintso Dodecahedrao Max Internal volumeo Rotations-120 Complexity-12 face-Actuation Direct Communication
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Simulations
Asynchronous operation Behaviour execution Different random order Denial of movement Notification to control programs No power limits
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How a module samples from the availabe moves??
Equal probability Directed random move Positive dot product E.g.direction of ball
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Control Primitives
Growth Seed Scents Mode-present FSM
Modules-search,seed,Final,Node
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Recursive branching for Locomotion & Manipulation
SLEEP SEARCH SEED FINAL NODE-spawns seeds INODE-emit node scent & propagates
regular scent
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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
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Contd..
Probabilistic approach Avoids oscillations Time order -200 cycles Probability to change state-1/R Impact on real world?????????
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Grasping objects
SLEEP SEARCH SEED TOUCH & TOUCHSEED FINAL
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Local minima & Stability
Physical motion Constraints Surface scent Alter the design Careful reconfiguration rules
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Conclusion
Local simple rules Genetic algorithms & FPGA Protein motors
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The USC/ISI CONRO Project