Handle URI:
http://hdl.handle.net/10754/600140
Title:
UOBPRM: A uniformly distributed obstacle-based PRM
Authors:
Yeh, Hsin-Yi; Thomas, Shawna; Eppstein, David; Amato, Nancy M.
Abstract:
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.
Citation:
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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
KAUST Grant Number:
KUS-C1–016–04
Issue Date:
Oct-2012
DOI:
10.1109/iros.2012.6385875
Type:
Conference Paper
Sponsors:
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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorYeh, Hsin-Yien
dc.contributor.authorThomas, Shawnaen
dc.contributor.authorEppstein, Daviden
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-28T06:43:33Zen
dc.date.available2016-02-28T06:43:33Zen
dc.date.issued2012-10en
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.en
dc.identifier.doi10.1109/iros.2012.6385875en
dc.identifier.urihttp://hdl.handle.net/10754/600140en
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.en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleUOBPRM: A uniformly distributed obstacle-based PRMen
dc.typeConference Paperen
dc.identifier.journal2012 IEEE/RSJ International Conference on Intelligent Robots and Systemsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionUC Irvine, Irvine, United Statesen
kaust.grant.numberKUS-C1–016–04en
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