Handle URI:
http://hdl.handle.net/10754/597398
Title:
A scalable distributed RRT for motion planning
Authors:
Jacobs, Sam Ade; Stradford, Nicholas; Rodriguez, Cesar; Thomas, Shawna; Amato, Nancy M.
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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE International Conference on Robotics and Automation
KAUST Grant Number:
KUSC1-016-04
Issue Date:
May-2013
DOI:
10.1109/ICRA.2013.6631304
Type:
Conference Paper
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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorJacobs, Sam Adeen
dc.contributor.authorStradford, Nicholasen
dc.contributor.authorRodriguez, Cesaren
dc.contributor.authorThomas, Shawnaen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T12:32:23Zen
dc.date.available2016-02-25T12:32:23Zen
dc.date.issued2013-05en
dc.identifier.citationJacobs 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.en
dc.identifier.doi10.1109/ICRA.2013.6631304en
dc.identifier.urihttp://hdl.handle.net/10754/597398en
dc.description.abstractRapidly-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.en
dc.description.sponsorshipThis 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleA scalable distributed RRT for motion planningen
dc.typeConference Paperen
dc.identifier.journal2013 IEEE International Conference on Robotics and Automationen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUSC1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.