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Conference Paper: Faster sample-based motion planning using instance-based learning
Title | Faster sample-based motion planning using instance-based learning |
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Authors | |
Issue Date | 2013 |
Publisher | Springer. |
Citation | Tenth International Workshop on the Algorithmic Foundations of Robotics (WAFR), Cambridge, MA, 13-15 June 2012. In Frazzoli, E, Lozano-Perez, T, Roy, N, Rus, D (Eds.), Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics, p. 381-396. Berlin: Springer, 2013 How to Cite? |
Abstract | We present a novel approach to improve the performance of sample-based motion planners by learning from prior instances. Our formulation stores the results of prior collision and local planning queries. This information is used to accelerate the performance of planners based on probabilistic collision checking, select new local paths in free space, and compute an efficient order to perform queries along a search path in a graph. We present fast and novel algorithms to perform k-NN (k-nearest neighbor) queries in high dimensional configuration spaces based on locality-sensitive hashing and derive tight bounds on their accuracy. The k-NN queries are used to perform instance-based learning and have a sub-linear time complexity. Our approach is general, makes no assumption about the sampling scheme, and can be used with various sample-based motion planners, including PRM, Lazy- PRM, RRT and RRT, by making small changes to these planners.We observe up to 100% improvement in the performance of various planners on rigid and articulated robots. |
Persistent Identifier | http://hdl.handle.net/10722/308713 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.485 |
Series/Report no. | Springer Tracts in Advanced Robotics ; 86 |
DC Field | Value | Language |
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dc.contributor.author | Pan, Jia | - |
dc.contributor.author | Chitta, Sachin | - |
dc.contributor.author | Manocha, Dinesh | - |
dc.date.accessioned | 2021-12-08T07:49:58Z | - |
dc.date.available | 2021-12-08T07:49:58Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Tenth International Workshop on the Algorithmic Foundations of Robotics (WAFR), Cambridge, MA, 13-15 June 2012. In Frazzoli, E, Lozano-Perez, T, Roy, N, Rus, D (Eds.), Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics, p. 381-396. Berlin: Springer, 2013 | - |
dc.identifier.isbn | 9783642362781 | - |
dc.identifier.issn | 1610-7438 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308713 | - |
dc.description.abstract | We present a novel approach to improve the performance of sample-based motion planners by learning from prior instances. Our formulation stores the results of prior collision and local planning queries. This information is used to accelerate the performance of planners based on probabilistic collision checking, select new local paths in free space, and compute an efficient order to perform queries along a search path in a graph. We present fast and novel algorithms to perform k-NN (k-nearest neighbor) queries in high dimensional configuration spaces based on locality-sensitive hashing and derive tight bounds on their accuracy. The k-NN queries are used to perform instance-based learning and have a sub-linear time complexity. Our approach is general, makes no assumption about the sampling scheme, and can be used with various sample-based motion planners, including PRM, Lazy- PRM, RRT and RRT, by making small changes to these planners.We observe up to 100% improvement in the performance of various planners on rigid and articulated robots. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics | - |
dc.relation.ispartofseries | Springer Tracts in Advanced Robotics ; 86 | - |
dc.title | Faster sample-based motion planning using instance-based learning | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-36279-8_23 | - |
dc.identifier.scopus | eid_2-s2.0-85009471302 | - |
dc.identifier.spage | 381 | - |
dc.identifier.epage | 396 | - |
dc.identifier.eissn | 1610-742X | - |
dc.publisher.place | Berlin | - |