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dc.contributor.authorMahadevan, Aditya
dc.contributor.authorAmato, Nancy M.
dc.date.accessioned2016-02-25T12:32:22Z
dc.date.available2016-02-25T12:32:22Z
dc.date.issued2012-05
dc.identifier.citationMahadevan A, Amato NM (2012) A sampling-based approach to probabilistic pursuit evasion. 2012 IEEE International Conference on Robotics and Automation. Available: http://dx.doi.org/10.1109/ICRA.2012.6225217.
dc.identifier.doi10.1109/ICRA.2012.6225217
dc.identifier.urihttp://hdl.handle.net/10754/597397
dc.description.abstractProbabilistic roadmaps (PRMs) are a sampling-based approach to motion-planning that encodes feasible paths through the environment using a graph created from a subset of valid positions. Prior research has shown that PRMs can be augmented with useful information to model interesting scenarios related to multi-agent interaction and coordination. © 2012 IEEE.
dc.description.sponsorshipThis research supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266 by THECB NHARPaward 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and byAward KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleA sampling-based approach to probabilistic pursuit evasion
dc.typeConference Paper
dc.identifier.journal2012 IEEE International Conference on Robotics and Automation
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-C1-016-04


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