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Conference Paper: G-Planner: Real-time motion planning and global navigation using GPUs
Title | G-Planner: Real-time motion planning and global navigation using GPUs |
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Authors | |
Issue Date | 2010 |
Citation | Proceedings of the National Conference on Artificial Intelligence, 2010, v. 2, p. 1245-1251 How to Cite? |
Abstract | We present novel randomized algorithms for solving global motion planning problems that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries and graph search. This approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms. We demonstrate the efficiency of our algorithms by applying them to a number of 6DOF planning benchmarks in 3D environments. Overall, this is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/206243 |
DC Field | Value | Language |
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dc.contributor.author | Pan, Jia | - |
dc.contributor.author | Lauterbach, Christian | - |
dc.contributor.author | Manocha, Dinesh | - |
dc.date.accessioned | 2014-10-22T01:25:30Z | - |
dc.date.available | 2014-10-22T01:25:30Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of the National Conference on Artificial Intelligence, 2010, v. 2, p. 1245-1251 | - |
dc.identifier.uri | http://hdl.handle.net/10722/206243 | - |
dc.description.abstract | We present novel randomized algorithms for solving global motion planning problems that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries and graph search. This approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms. We demonstrate the efficiency of our algorithms by applying them to a number of 6DOF planning benchmarks in 3D environments. Overall, this is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the National Conference on Artificial Intelligence | - |
dc.title | G-Planner: Real-time motion planning and global navigation using GPUs | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-77958519673 | - |
dc.identifier.volume | 2 | - |
dc.identifier.spage | 1245 | - |
dc.identifier.epage | 1251 | - |