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Conference Paper: GPU-based parallel collision detection for real-time motion planning

TitleGPU-based parallel collision detection for real-time motion planning
Authors
Issue Date2010
Citation
Springer Tracts in Advanced Robotics, 2010, v. 68, n. STAR, p. 211-228 How to Cite?
AbstractWe present parallel algorithms to accelerate collision queries for sample-based motion planning. Our approach is designed for current many-core GPUs and exploits the data-parallelism and multi-threaded capabilities. In order to take advantage of high number of cores, we present a clustering scheme and collision-packet traversal to perform efficient collision queries on multiple configurations simultaneously. Furthermore, we present a hierarchical traversal scheme that performs workload balancing for high parallel efficiency. We have implemented our algorithms on commodity NVIDIA GPUs using CUDA and can perform 500,000 collision queries/second on our benchmarks, which is 10X faster than prior GPU-based techniques. Moreover, we can compute collision-free paths for rigid and articulated models in less than 100 milliseconds for many benchmarks, almost 50-100X faster than current CPU-based planners. © 2010 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/206247
ISSN
2015 SCImago Journal Rankings: 0.200

 

DC FieldValueLanguage
dc.contributor.authorPan, Jia-
dc.contributor.authorManocha, Dinesh-
dc.date.accessioned2014-10-22T01:25:31Z-
dc.date.available2014-10-22T01:25:31Z-
dc.date.issued2010-
dc.identifier.citationSpringer Tracts in Advanced Robotics, 2010, v. 68, n. STAR, p. 211-228-
dc.identifier.issn1610-7438-
dc.identifier.urihttp://hdl.handle.net/10722/206247-
dc.description.abstractWe present parallel algorithms to accelerate collision queries for sample-based motion planning. Our approach is designed for current many-core GPUs and exploits the data-parallelism and multi-threaded capabilities. In order to take advantage of high number of cores, we present a clustering scheme and collision-packet traversal to perform efficient collision queries on multiple configurations simultaneously. Furthermore, we present a hierarchical traversal scheme that performs workload balancing for high parallel efficiency. We have implemented our algorithms on commodity NVIDIA GPUs using CUDA and can perform 500,000 collision queries/second on our benchmarks, which is 10X faster than prior GPU-based techniques. Moreover, we can compute collision-free paths for rigid and articulated models in less than 100 milliseconds for many benchmarks, almost 50-100X faster than current CPU-based planners. © 2010 Springer-Verlag Berlin Heidelberg.-
dc.languageeng-
dc.relation.ispartofSpringer Tracts in Advanced Robotics-
dc.titleGPU-based parallel collision detection for real-time motion planning-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-17452-0_13-
dc.identifier.scopuseid_2-s2.0-78650146189-
dc.identifier.volume68-
dc.identifier.issueSTAR-
dc.identifier.spage211-
dc.identifier.epage228-
dc.identifier.eissn1610-742X-

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