Improved roadmap connection via local learning for sampling based planners

Handle URI:
http://hdl.handle.net/10754/598580
Title:
Improved roadmap connection via local learning for sampling based planners
Authors:
Ekenna, Chinwe; Uwacu, Diane; Thomas, Shawna; Amato, Nancy M.
Citation:
Ekenna C, Uwacu D, Thomas S, Amato NM (2015) Improved roadmap connection via local learning for sampling based planners. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Available: http://dx.doi.org/10.1109/IROS.2015.7353825.
Publisher:
IEEE
Journal:
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Sep-2015
DOI:
10.1109/IROS.2015.7353825
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, by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST), by NSF broadening participation in computing program (NSFCNS-0540631) and by the Schlumberger Faculty for the Future Fellowship.This research used resources of the National Energy Research ScientificComputing Center, which is supported by the Office of Science of the 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.authorEkenna, Chinween
dc.contributor.authorUwacu, Dianeen
dc.contributor.authorThomas, Shawnaen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T13:32:31Zen
dc.date.available2016-02-25T13:32:31Zen
dc.date.issued2015-09en
dc.identifier.citationEkenna C, Uwacu D, Thomas S, Amato NM (2015) Improved roadmap connection via local learning for sampling based planners. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Available: http://dx.doi.org/10.1109/IROS.2015.7353825.en
dc.identifier.doi10.1109/IROS.2015.7353825en
dc.identifier.urihttp://hdl.handle.net/10754/598580en
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, by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST), by NSF broadening participation in computing program (NSFCNS-0540631) and by the Schlumberger Faculty for the Future Fellowship.This research used resources of the National Energy Research ScientificComputing Center, which is supported by the Office of Science of the U.S.Department of Energy under Contract No. DE-AC02-05CH11231.en
dc.publisherIEEEen
dc.titleImproved roadmap connection via local learning for sampling based plannersen
dc.typeConference Paperen
dc.identifier.journal2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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