Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

Adam Fidel, Sam Ade Jacobs, Shishir Sharma, Nancy M. Amato, Lawrence Rauchwerger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.
Original languageEnglish (US)
Title of host publication2014 IEEE 28th International Parallel and Distributed Processing Symposium
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages573-582
Number of pages10
ISBN (Print)9781479938001
DOIs
StatePublished - May 2014
Externally publishedYes

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