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- Publisher Website: 10.1007/978-3-642-17452-0_13
- Scopus: eid_2-s2.0-78650146189
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Conference Paper: GPU-based parallel collision detection for real-time motion planning
Title | GPU-based parallel collision detection for real-time motion planning |
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Authors | |
Issue Date | 2010 |
Citation | Springer Tracts in Advanced Robotics, 2010, v. 68, n. STAR, p. 211-228 How to Cite? |
Abstract | We 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 Identifier | http://hdl.handle.net/10722/206247 |
ISSN | 2020 SCImago Journal Rankings: 0.485 |
DC Field | Value | Language |
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dc.contributor.author | Pan, Jia | - |
dc.contributor.author | Manocha, Dinesh | - |
dc.date.accessioned | 2014-10-22T01:25:31Z | - |
dc.date.available | 2014-10-22T01:25:31Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Springer Tracts in Advanced Robotics, 2010, v. 68, n. STAR, p. 211-228 | - |
dc.identifier.issn | 1610-7438 | - |
dc.identifier.uri | http://hdl.handle.net/10722/206247 | - |
dc.description.abstract | We 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.language | eng | - |
dc.relation.ispartof | Springer Tracts in Advanced Robotics | - |
dc.title | GPU-based parallel collision detection for real-time motion planning | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-17452-0_13 | - |
dc.identifier.scopus | eid_2-s2.0-78650146189 | - |
dc.identifier.volume | 68 | - |
dc.identifier.issue | STAR | - |
dc.identifier.spage | 211 | - |
dc.identifier.epage | 228 | - |
dc.identifier.eissn | 1610-742X | - |
dc.identifier.issnl | 1610-7438 | - |