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A new technique for improving the dispersion of a set of samples. Application in multi-query motion planning

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dc.contributor.authorKhaksar W.en_US
dc.contributor.authorHong T.S.en_US
dc.contributor.authorSahari K.S.B.M.en_US
dc.contributor.authorKhaksar M.en_US
dc.contributor.authorid54960984900en_US
dc.contributor.authorid8231495000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid55350135000en_US
dc.date.accessioned2023-05-29T06:00:20Z
dc.date.available2023-05-29T06:00:20Z
dc.date.issued2015
dc.description.abstractIn this paper, we proposed a new learning strategy for probabilistic roadmap (PRM) algorithm. The proposed strategy is based on reducing the dispersion of the generated set of samples. We defined a forbidden range around each selected sample and ignore this region in further sampling. The resulted planner called LD-PRM is an effective multi-query sampling-based planner which is able to solve motion planning queries with smaller graphs. Simulation results indicated that the proposed planner improve the runtime of the PRM algorithm. Furthermore, the proposed planner is able to solve difficult motion planning cases including narrow passages and bug traps, which is a difficult task for classic sampling-based algorithms. For measuring the uniformity of the generated samples, a new algorithm was created to measure the dispersion of a set of samples based on any desired resolution. Also, comparison studies are provided to support the superiority claim of the proposed algorithm. � 2015 AIP Publishing LLC.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo90034
dc.identifier.doi10.1063/1.4915878
dc.identifier.scopus2-s2.0-84954359122
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84954359122&doi=10.1063%2f1.4915878&partnerID=40&md5=ea5e9295d59c307b67f673e1ec2712a2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22341
dc.identifier.volume1660
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleA new technique for improving the dispersion of a set of samples. Application in multi-query motion planningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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