A sampling-based approach to probabilistic pursuit evasion

Aditya Mahadevan; Nancy M. Amato

doi:10.1109/ICRA.2012.6225217

A sampling-based approach to probabilistic pursuit evasion

Aditya Mahadevan, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	2012 IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3192-3199
Number of pages	8
ISBN (Print)	9781467314053
DOIs	https://doi.org/10.1109/ICRA.2012.6225217
State	Published - May 2012
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1109/ICRA.2012.6225217

Cite this

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title = "A sampling-based approach to probabilistic pursuit evasion",

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. {\textcopyright} 2012 IEEE.",

author = "Aditya Mahadevan and Amato, {Nancy M.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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). This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2012",

month = may,

doi = "10.1109/ICRA.2012.6225217",

language = "English (US)",

isbn = "9781467314053",

pages = "3192--3199",

booktitle = "2012 IEEE International Conference on Robotics and Automation",

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AU - Amato, Nancy M.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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). This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/597397

UR - http://ieeexplore.ieee.org/document/6225217/

UR - http://www.scopus.com/inward/record.url?scp=84864440734&partnerID=8YFLogxK

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DO - 10.1109/ICRA.2012.6225217

M3 - Conference contribution

SN - 9781467314053

SP - 3192

EP - 3199

BT - 2012 IEEE International Conference on Robotics and Automation

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

A sampling-based approach to probabilistic pursuit evasion

Abstract

Bibliographical note

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