Handle URI:
http://hdl.handle.net/10754/597397
Title:
A sampling-based approach to probabilistic pursuit evasion
Authors:
Mahadevan, Aditya; Amato, Nancy M.
Abstract:
Probabilistic 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.
Citation:
Mahadevan 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 IEEE International Conference on Robotics and Automation
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
May-2012
DOI:
10.1109/ICRA.2012.6225217
Type:
Conference Paper
Sponsors:
This 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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMahadevan, Adityaen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T12:32:22Zen
dc.date.available2016-02-25T12:32:22Zen
dc.date.issued2012-05en
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.en
dc.identifier.doi10.1109/ICRA.2012.6225217en
dc.identifier.urihttp://hdl.handle.net/10754/597397en
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.en
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).en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleA sampling-based approach to probabilistic pursuit evasionen
dc.typeConference Paperen
dc.identifier.journal2012 IEEE International Conference on Robotics and Automationen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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