KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/599875
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Abstract© 2014 IEEE. In this paper, we evaluate and compare the quality and structure of roadmaps constructed from parallelizing sampling-based motion planning algorithms against that of roadmaps constructed using sequential planner. Also, we make an argument and provide experimental results that show that motion planning problems involving heterogenous environments (common in most realistic and large-scale motion planning) is a natural fit for spatial subdivision-based parallel processing. Spatial subdivision-based parallel processing approach is suited for heterogeneous environments because it allows for local adaption in solving a global problem while taking advantage of scalability that is possible with parallel processing.
CitationJacobs SA, Amato NM (2014) The anatomy of a distributed motion planning roadmap. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2014.6942979.
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 and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST). This research used resources of the National Energy ResearchScientific Computing Center, which is supported by the Office of Science ofthe U.S. Department of Energy under Contract No. DE-AC02-05CH11231.