V-Plan: A Voronoi-Based Hybrid Motion Planner
	Mark Foskey	foskey@cs.unc.edu
	Maxim Garber	garber@cs.unc.edu
	Ming C. Lin	lin@cs.unc.edu
	Dinesh Manocha	dm@cs.umd.edu

Abstract: We present a hybrid path planning algorithm for rigid and articulated bodies translating and rotating in a 3D workspace. Our approach generates a Voronoi roadmap in the workspace and combines it with ``bridges'' computed by a randomized path planner with Voronoi-biased sampling. The Voronoi roadmap is computed from a discrete approximation to the generalized Voronoi diagram (GVD) of the workspace, which is generated using graphics hardware. By this use of the GVD, portions of the path can be generated without random sampling, substantially reducing the number of random samples needed for the full query. The planner has been implemented and tested on a number of benchmarks. Some preliminary comparisons with a randomized motion planner indicate that our planner performs more than an order of magnitude faster in several challenging scenarios.

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