General Motion Planning Methods for Self-Reconfiguration Planning
Self-reconfiguring robotic systems (SRSs) can alter their morphology autonomously. Determining a feasible plan of subcomponent moves that realize a desired shape, in general, is a hard problem for which there are no general solutions. We investigated the utility of some general motion planning methods, namely greedy search, RRT-Connect (RRT), probabilistic roadmaps (PRM) and simulated annealing (SA), as part of an investigation into generally applicable techniques for different SRS architectures. The performance of such methods is greatly dependent on heuristics. We present two new heuristics that improve performance, a greedy assignment heuristic which is a faster approximation to the classic optimal assignment heuristic, and the vector map heuristic, which transforms a configuration into a vector representation for fast nearest neighbor queries. Results of our experiments show greedy search is the fastest single shot planning algorithm for two variants of the hexagonal metamorphic system. Probabilistic roadmap planning is the fastest method overall, but initial roadmap construction is expensive. Also, we applied two existing post processing smoothing algorithms whose combination significantly improves plans produced by RRT, SA and PRM.