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Conference Paper: Closing the loop between motion planning and task execution using real-time GPU-based planners
Title | Closing the loop between motion planning and task execution using real-time GPU-based planners |
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Authors | |
Issue Date | 2010 |
Citation | AAAI Workshop - Technical Report, 2010, v. WS-10-01, p. 43-47 How to Cite? |
Abstract | Many task execution techniques tend to repeatedly invoke motion planning algorithms in order to perform complex tasks. In order to accelerate the perform of such methods, we present a real-time global motion planner that utilizes the computational capabilities of current many-core GPUs (graphics processing units). Our approach is based on randomized sample-based planners and we describe highly parallel algorithms to generate samples, perform collision queries, nearest-neighbor computations, local planning and graph search to compute collision-free paths for rigid robots. Our approach can efficiently solve the single-query and multiquery versions of the planning problem and can obtain one to two orders of speedup over prior CPU-based global planning algorithms. The resulting GPU-based planning algorithm can also be used for real-time feedback for task execution in challenging scenarios. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/206255 |
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 | AAAI Workshop - Technical Report, 2010, v. WS-10-01, p. 43-47 | - |
dc.identifier.uri | http://hdl.handle.net/10722/206255 | - |
dc.description.abstract | Many task execution techniques tend to repeatedly invoke motion planning algorithms in order to perform complex tasks. In order to accelerate the perform of such methods, we present a real-time global motion planner that utilizes the computational capabilities of current many-core GPUs (graphics processing units). Our approach is based on randomized sample-based planners and we describe highly parallel algorithms to generate samples, perform collision queries, nearest-neighbor computations, local planning and graph search to compute collision-free paths for rigid robots. Our approach can efficiently solve the single-query and multiquery versions of the planning problem and can obtain one to two orders of speedup over prior CPU-based global planning algorithms. The resulting GPU-based planning algorithm can also be used for real-time feedback for task execution in challenging scenarios. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | AAAI Workshop - Technical Report | - |
dc.title | Closing the loop between motion planning and task execution using real-time GPU-based planners | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-79959741290 | - |
dc.identifier.volume | WS-10-01 | - |
dc.identifier.spage | 43 | - |
dc.identifier.epage | 47 | - |