UOBPRM: A uniformly distributed obstacle-based PRM

dc.contributor.authorYeh, Hsin-Yi
dc.contributor.authorThomas, Shawna
dc.contributor.authorEppstein, David
dc.contributor.authorAmato, Nancy M.
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionUC Irvine, Irvine, United States
dc.date.accessioned2016-02-28T06:43:33Z
dc.date.available2016-02-28T06:43:33Z
dc.date.issued2012-10
dc.description.abstractThis paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages. © 2012 IEEE.
dc.description.sponsorshipThe work of Yeh, Thomas and Amato supported in part by NSF Grants EIA-OI03742, ACR-0081510, ACR-0113971, CCR-0113974, ACI-0326350, CRI-0551685, CCF-0833199, CCF-0830753, by the DOE, Chevron, IBM, Intel, HP, and by King Abdullah University of Science and Technology (KAUST) Award KUS-C1–016–04. The work of Eppstein supported in part by NSF Grants 0830403 and 1217322, and by the Office of Naval Research under MURI grant N00014–08-1–1015.
dc.identifier.citationYeh H-Y, Thomas S, Eppstein D, Amato NM (2012) UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/iros.2012.6385875.
dc.identifier.doi10.1109/iros.2012.6385875
dc.identifier.journal2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
dc.identifier.urihttp://hdl.handle.net/10754/600140
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleUOBPRM: A uniformly distributed obstacle-based PRM
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Yeh, Hsin-Yi,equals">Yeh, Hsin-Yi</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Thomas, Shawna,equals">Thomas, Shawna</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Eppstein, David,equals">Eppstein, David</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Amato, Nancy M.,equals">Amato, Nancy M.</a><br><br><h5>KAUST Grant Number</h5>KUS-C1–016–04<br><br><h5>Date</h5>2012-10</span>
display.details.right<span><h5>Abstract</h5>This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages. © 2012 IEEE.<br><br><h5>Citation</h5>Yeh H-Y, Thomas S, Eppstein D, Amato NM (2012) UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/iros.2012.6385875.<br><br><h5>Acknowledgements</h5>The work of Yeh, Thomas and Amato supported in part by NSF Grants EIA-OI03742, ACR-0081510, ACR-0113971, CCR-0113974, ACI-0326350, CRI-0551685, CCF-0833199, CCF-0830753, by the DOE, Chevron, IBM, Intel, HP, and by King Abdullah University of Science and Technology (KAUST) Award KUS-C1–016–04. The work of Eppstein supported in part by NSF Grants 0830403 and 1217322, and by the Office of Naval Research under MURI grant N00014–08-1–1015.<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Institute of Electrical and Electronics Engineers (IEEE),equals">Institute of Electrical and Electronics Engineers (IEEE)</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=2012 IEEE/RSJ International Conference on Intelligent Robots and Systems,equals">2012 IEEE/RSJ International Conference on Intelligent Robots and Systems</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1109/iros.2012.6385875">10.1109/iros.2012.6385875</a></span>
kaust.grant.numberKUS-C1–016–04
orcid.authorYeh, Hsin-Yi
orcid.authorThomas, Shawna
orcid.authorEppstein, David
orcid.authorAmato, Nancy M.
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