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

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. Recen...

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Vydáno v:Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 573 - 582
Hlavní autoři: Fidel, Adam, Jacobs, Sam Ade, Sharma, Shishir, Amato, Nancy M., Rauchwerger, Lawrence
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2014
Témata:
Joining processes
Load management
Measurement
Planning
Probabilistic logic
Program processors
Proteins
ISBN:1479937991, 9781479937998
ISSN:1530-2075
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Shrnutí: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.
ISBN:1479937991
9781479937998
ISSN:1530-2075
DOI:10.1109/IPDPS.2014.66