Probabilistic roadmap methods are embarrassingly parallel
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In this paper we report on our experience parallelizing probabilistic roadmap motion planning methods (PRMs). We show that significant, scalable speedups can be obtained with relatively little effort on the part of the developer. Our experience is not limited to PRMs, however. In particular, we outline general techniques for parallelizing types of computations commonly performed in motion planning algorithms, and identify potential difficulties that might be faced in other efforts to parallelize sequential motion planning methods.
