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    A scalable distributed RRT for motion planning

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    Type
    Conference Paper
    Authors
    Jacobs, Sam Ade
    Stradford, Nicholas
    Rodriguez, Cesar
    Thomas, Shawna
    Amato, Nancy M.
    KAUST Grant Number
    KUSC1-016-04
    Date
    2013-05
    Permanent link to this record
    http://hdl.handle.net/10754/597398
    
    Metadata
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    Abstract
    Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine. © 2013 IEEE.
    Citation
    Jacobs SA, Stradford N, Rodriguez C, Thomas S, Amato NM (2013) A scalable distributed RRT for motion planning. 2013 IEEE International Conference on Robotics and Automation. Available: http://dx.doi.org/10.1109/ICRA.2013.6631304.
    Sponsors
    This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, by NSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCIR25 CA090301-11, by DOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, by THECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUSC1-016-04, made by King Abdullah University of Science and Technology(KAUST). This research used resources of the National Energy ResearchScientific Computing Center, which is supported by the Office of Science ofthe U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 IEEE International Conference on Robotics and Automation
    DOI
    10.1109/ICRA.2013.6631304
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICRA.2013.6631304
    Scopus Count
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