PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

General Motion Planning Methods for Self-Reconfiguration Planning
Thomas Larkworthy, Gillian Hayes and Subramanian Ramamoorthy
In: Towards Autonomous Robotic Systems (TAROS 2009), 31 Aug - 2 Sep, 2009, Londonderry, United Kingdom.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6080
Deposited By:Subramanian Ramamoorthy
Deposited On:08 March 2010