Improved roadmap connection via local learning for sampling based planners
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/598580
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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.
SponsorsThis 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.