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    Improved roadmap connection via local learning for sampling based planners

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    Type
    Conference Paper
    Authors
    Ekenna, Chinwe
    Uwacu, Diane
    Thomas, Shawna
    Amato, Nancy M.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2015-09
    Permanent link to this record
    http://hdl.handle.net/10754/598580
    
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    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.
    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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
    DOI
    10.1109/IROS.2015.7353825
    ae974a485f413a2113503eed53cd6c53
    10.1109/IROS.2015.7353825
    Scopus Count
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