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

Assumptions Limited computational capabilities Limited Memory Simple FSM No global broadcast Limited communication

Experimental Robot Platform Proteo-Modular self reconfigurable robot

Polypod

Features Substrate reconfiguration Geometric constraints o Dodecahedra o Max Internal volume o 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 scent Root 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