Handle URI:
http://hdl.handle.net/10754/597681
Title:
Blind RRT: A probabilistically complete distributed RRT
Authors:
Rodriguez, Cesar; Denny, Jory; Jacobs, Sam Ade; Thomas, Shawna; Amato, Nancy M.
Abstract:
Rapidly-Exploring Random Trees (RRTs) have been successful at finding feasible solutions for many types of problems. With motion planning becoming more computationally demanding, we turn to parallel motion planning for efficient solutions. Existing work on distributed RRTs has been limited by the overhead that global communication requires. A recent approach, Radial RRT, demonstrated a scalable algorithm that subdivides the space into regions to increase the computation locality. However, if an obstacle completely blocks RRT growth in a region, the planning space is not covered and is thus not probabilistically complete. We present a new algorithm, Blind RRT, which ignores obstacles during initial growth to efficiently explore the entire space. Because obstacles are ignored, free components of the tree become disconnected and fragmented. Blind RRT merges parts of the tree that have become disconnected from the root. We show how this algorithm can be applied to the Radial RRT framework allowing both scalability and effectiveness in motion planning. This method is a probabilistically complete approach to parallel RRTs. We show that our method not only scales but also overcomes the motion planning limitations that Radial RRT has in a series of difficult motion planning tasks. © 2013 IEEE.
Citation:
Rodriguez C, Denny J, Jacobs SA, Thomas S, Amato NM (2013) Blind RRT: A probabilistically complete distributed RRT. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2013.6696587.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
KAUST Grant Number:
KUSC1-016-04
Issue Date:
Nov-2013
DOI:
10.1109/IROS.2013.6696587
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.authorRodriguez, Cesaren
dc.contributor.authorDenny, Joryen
dc.contributor.authorJacobs, Sam Adeen
dc.contributor.authorThomas, Shawnaen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T12:44:18Zen
dc.date.available2016-02-25T12:44:18Zen
dc.date.issued2013-11en
dc.identifier.citationRodriguez C, Denny J, Jacobs SA, Thomas S, Amato NM (2013) Blind RRT: A probabilistically complete distributed RRT. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2013.6696587.en
dc.identifier.doi10.1109/IROS.2013.6696587en
dc.identifier.urihttp://hdl.handle.net/10754/597681en
dc.description.abstractRapidly-Exploring Random Trees (RRTs) have been successful at finding feasible solutions for many types of problems. With motion planning becoming more computationally demanding, we turn to parallel motion planning for efficient solutions. Existing work on distributed RRTs has been limited by the overhead that global communication requires. A recent approach, Radial RRT, demonstrated a scalable algorithm that subdivides the space into regions to increase the computation locality. However, if an obstacle completely blocks RRT growth in a region, the planning space is not covered and is thus not probabilistically complete. We present a new algorithm, Blind RRT, which ignores obstacles during initial growth to efficiently explore the entire space. Because obstacles are ignored, free components of the tree become disconnected and fragmented. Blind RRT merges parts of the tree that have become disconnected from the root. We show how this algorithm can be applied to the Radial RRT framework allowing both scalability and effectiveness in motion planning. This method is a probabilistically complete approach to parallel RRTs. We show that our method not only scales but also overcomes the motion planning limitations that Radial RRT has in a series of difficult motion planning tasks. © 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.titleBlind RRT: A probabilistically complete distributed RRTen
dc.typeConference Paperen
dc.identifier.journal2013 IEEE/RSJ International Conference on Intelligent Robots and Systemsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUSC1-016-04en
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