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Conference Paper: Poisson-RRT

TitlePoisson-RRT
Authors
Park, ChonhyonPan, JiaManocha, Dinesh
Issue Date2014
Citation
2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May-7 June 2014. In Conference Proceedings, 2014, p. 4667-4673 How to Cite?
DOI: http://dx.doi.org/10.1109/ICRA.2014.6907541
AbstractWe present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.
Persistent Identifierhttp://hdl.handle.net/10722/308854
ISSN
1050-4729
2023 SCImago Journal Rankings: 1.620
ISI Accession Number ID
WOS:000377221104106

 

DC FieldValueLanguage
dc.contributor.authorPark, Chonhyon-
dc.contributor.authorPan, Jia-
dc.contributor.authorManocha, Dinesh-
dc.date.accessioned2021-12-08T07:50:16Z-
dc.date.available2021-12-08T07:50:16Z-
dc.date.issued2014-
dc.identifier.citation2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May-7 June 2014. In Conference Proceedings, 2014, p. 4667-4673-
dc.identifier.issn1050-4729-
dc.identifier.urihttp://hdl.handle.net/10722/308854-
dc.description.abstractWe present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.-
dc.languageeng-
dc.relation.ispartof2014 IEEE International Conference on Robotics and Automation (ICRA)-
dc.titlePoisson-RRT-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICRA.2014.6907541-
dc.identifier.scopuseid_2-s2.0-84929225015-
dc.identifier.spage4667-
dc.identifier.epage4673-
dc.identifier.isiWOS:000377221104106-

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