Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations

Autores
Stanhouse, Kyle; Kitts, Chris; Mas, Ignacio Agustin
Año de publicación
2016
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
The motions of a formation of mobile robots along predetermined paths are optimized according to a tunable time-energy cost function using the cluster space approach to multiagent system specification and control. Upon path-parameterizing cluster state variables describing the geometry and pose of a multirobot group, an optimal control problem is formulated that incorporates formation dynamics and state constraints. The optimal trajectory is derived numerically via a gradient search, iterating over the initial value of one costate. A multirobot formation control simulation is then used to demonstrate the effectiveness of the technique. Results indicate that a substantial tradeoff is made between energy expenditure and motion time when considered as minimization criteria in varying proportions, allowing the operator to tailor mission trajectories according to desired levels of each.
Fil: Stanhouse, Kyle. No especifíca;
Fil: Kitts, Chris. No especifíca;
Fil: Mas, Ignacio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; Argentina
Materia
mobile robots
time-energy optimization
motion planning
multi-robot systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/107920
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/107920
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot FormationsStanhouse, KyleKitts, ChrisMas, Ignacio Agustinmobile robotstime-energy optimizationmotion planningmulti-robot systemshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The motions of a formation of mobile robots along predetermined paths are optimized according to a tunable time-energy cost function using the cluster space approach to multiagent system specification and control. Upon path-parameterizing cluster state variables describing the geometry and pose of a multirobot group, an optimal control problem is formulated that incorporates formation dynamics and state constraints. The optimal trajectory is derived numerically via a gradient search, iterating over the initial value of one costate. A multirobot formation control simulation is then used to demonstrate the effectiveness of the technique. Results indicate that a substantial tradeoff is made between energy expenditure and motion time when considered as minimization criteria in varying proportions, allowing the operator to tailor mission trajectories according to desired levels of each.Fil: Stanhouse, Kyle. No especifíca;Fil: Kitts, Chris. No especifíca;Fil: Mas, Ignacio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; ArgentinaIntechOpenWang, Guanghui2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookParthttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/107920Stanhouse, Kyle; Kitts, Chris; Mas, Ignacio Agustin; Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations; IntechOpen; 2016; 141-163978-953-51-2571-6CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.intechopen.com/books/recent-advances-in-robotic-systems/time-energy-optimal-cluster-space-motion-planning-for-mobile-robot-formationsinfo:eu-repo/semantics/altIdentifier/doi/10.5772/64615info:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/recent-advances-in-robotic-systemsinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:47:17Zoai:ri.conicet.gov.ar:11336/107920instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:47:17.601CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
title Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
spellingShingle Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
Stanhouse, Kyle
mobile robots
time-energy optimization
motion planning
multi-robot systems
title_short Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
title_full Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
title_fullStr Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
title_full_unstemmed Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
title_sort Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
dc.creator.none.fl_str_mv Stanhouse, Kyle
Kitts, Chris
Mas, Ignacio Agustin
author Stanhouse, Kyle
author_facet Stanhouse, Kyle
Kitts, Chris
Mas, Ignacio Agustin
author_role author
author2 Kitts, Chris
Mas, Ignacio Agustin
author2_role author
author
dc.contributor.none.fl_str_mv Wang, Guanghui
dc.subject.none.fl_str_mv mobile robots
time-energy optimization
motion planning
multi-robot systems
topic mobile robots
time-energy optimization
motion planning
multi-robot systems
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The motions of a formation of mobile robots along predetermined paths are optimized according to a tunable time-energy cost function using the cluster space approach to multiagent system specification and control. Upon path-parameterizing cluster state variables describing the geometry and pose of a multirobot group, an optimal control problem is formulated that incorporates formation dynamics and state constraints. The optimal trajectory is derived numerically via a gradient search, iterating over the initial value of one costate. A multirobot formation control simulation is then used to demonstrate the effectiveness of the technique. Results indicate that a substantial tradeoff is made between energy expenditure and motion time when considered as minimization criteria in varying proportions, allowing the operator to tailor mission trajectories according to desired levels of each.
Fil: Stanhouse, Kyle. No especifíca;
Fil: Kitts, Chris. No especifíca;
Fil: Mas, Ignacio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; Argentina
description The motions of a formation of mobile robots along predetermined paths are optimized according to a tunable time-energy cost function using the cluster space approach to multiagent system specification and control. Upon path-parameterizing cluster state variables describing the geometry and pose of a multirobot group, an optimal control problem is formulated that incorporates formation dynamics and state constraints. The optimal trajectory is derived numerically via a gradient search, iterating over the initial value of one costate. A multirobot formation control simulation is then used to demonstrate the effectiveness of the technique. Results indicate that a substantial tradeoff is made between energy expenditure and motion time when considered as minimization criteria in varying proportions, allowing the operator to tailor mission trajectories according to desired levels of each.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/bookPart
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
status_str publishedVersion
format bookPart
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/107920
Stanhouse, Kyle; Kitts, Chris; Mas, Ignacio Agustin; Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations; IntechOpen; 2016; 141-163
978-953-51-2571-6
CONICET Digital
CONICET
url http://hdl.handle.net/11336/107920
identifier_str_mv Stanhouse, Kyle; Kitts, Chris; Mas, Ignacio Agustin; Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations; IntechOpen; 2016; 141-163
978-953-51-2571-6
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.intechopen.com/books/recent-advances-in-robotic-systems/time-energy-optimal-cluster-space-motion-planning-for-mobile-robot-formations
info:eu-repo/semantics/altIdentifier/doi/10.5772/64615
info:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/recent-advances-in-robotic-systems
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IntechOpen
publisher.none.fl_str_mv IntechOpen
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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