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Multiagent control of Self-reconfigurable Robots - Hristo bojinov,Arancha Casal,Tad Hogg

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.

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Multiagent control of Self-reconfigurable Robots - Hristo bojinov,Arancha Casal,Tad Hogg

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  1. Multiagent control of Self-reconfigurable Robots -Hristo bojinov,Arancha Casal,Tad Hogg Harini Gurusamy

  2. Features • Identical simple Modules • Dynamic adaptation • Multiagent Control • De-Centralized Control • No complex sensors

  3. Real time e.g Ant Colony Amoeba

  4. Two approaches • Combinatorial Search • Identify motion of modules • 1.Module Specification • Not suitable to real time • 2.Agent based Control • Creating structures with properties • Decomposes control problems

  5. Assumptions • Limited computational capabilities • Limited Memory • Simple FSM • No global broadcast • Limited communication

  6. Experimental Robot Platform Proteo-Modular self reconfigurable robot

  7. Polypod

  8. Features • Substrate reconfiguration • Geometric constraints • Dodecahedra • Max Internal volume • Rotations-120 • Complexity-12 face-Actuation • Direct Communication

  9. Simulations • Asynchronous operation • Behaviour execution • Different random order • Denial of movement • Notification to control programs • No power limits

  10. How a module samples from the availabe moves?? • Equal probability • Directed random move • Positive dot product • E.g.direction of ball

  11. Control Primitives • Growth • Seed • Scents • Mode-present FSM Modules-search,seed,Final,Node

  12. Recursive branching for Locomotion & Manipulation • SLEEP • SEARCH • SEED • FINAL • NODE-spawns seeds • INODE-emit node scent & propagates regular scent

  13. Dynamic adaptation to external forces • Supporting weight on legs • Neglect weights of modules • 1 level and 2 level branching • Fixed Module • Transmit scent Root Module • IROOT-Avg wt > Fmax with Pmax • AROOT • ROOT-Avg wt < Fmin with Pmin

  14. Contd.. • Probabilistic approach • Avoids oscillations • Time order -200 cycles • Probability to change state-1/R • Impact on real world?????????

  15. Grasping objects • SLEEP • SEARCH • SEED • TOUCH & TOUCHSEED • FINAL

  16. Local minima & Stability • Physical motion Constraints • Surface scent • Alter the design • Careful reconfiguration rules

  17. Conclusion • Local simple rules • Genetic algorithms & FPGA • Protein motors

  18. The USC/ISI CONRO Project

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