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dc.contributor.authorMcMahon, Troy
dc.contributor.authorJacobs, Sam
dc.contributor.authorBoyd, Bryan
dc.contributor.authorTapia, Lydia
dc.contributor.authorAmato, Nancy M.
dc.date.accessioned2016-02-25T13:39:56Z
dc.date.available2016-02-25T13:39:56Z
dc.date.issued2012-10
dc.identifier.citationMcMahon T, Jacobs S, Boyd B, Tapia L, Amato NM (2012) Local randomization in neighbor selection improves PRM roadmap quality. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2012.6386061.
dc.identifier.doi10.1109/IROS.2012.6386061
dc.identifier.urihttp://hdl.handle.net/10754/598224
dc.description.abstractProbabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
dc.description.sponsorshipThis research supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266, by THECB NHARPaward 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and byAward KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST). Tapia supported in part by NIH Grant P20RR018754.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleLocal randomization in neighbor selection improves PRM roadmap quality
dc.typeConference Paper
dc.identifier.journal2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionUniversity of New Mexico, Albuquerque, United States
kaust.grant.numberKUS-C1-016-04


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