Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm

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Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO al...

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Detalles Bibliográficos
Autores: Uriol, R, Moran, A
Formato: objeto de conferencia
Fecha de Publicación:2017
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/1204
Enlace del recurso:https://hdl.handle.net/20.500.12390/1204
https://doi.org/10.1109/ICCAR.2017.7942653
Nivel de acceso:acceso abierto
Materia:path planning
ant colony optimization
mobile robots
https://purl.org/pe-repo/ocde/ford#4.03.00
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spelling Publicationrp03449600rp03448600Uriol, RMoran, A2024-05-30T23:13:38Z2024-05-30T23:13:38Z2017https://hdl.handle.net/20.500.12390/1204https://doi.org/10.1109/ICCAR.2017.7942653404256400003Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengInternational Conference on Control, Automation and Robotics (ICCAR)info:eu-repo/semantics/openAccesspath planningant colony optimization-1mobile robots-1https://purl.org/pe-repo/ocde/ford#4.03.00-1Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithminfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/1204oai:repositorio.concytec.gob.pe:20.500.12390/12042024-05-30 15:49:48.855http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9aa88885-448c-4e1b-b6bc-8655eb3e4ce2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <DOI>https://doi.org/10.1109/ICCAR.2017.7942653</DOI> <ISI-Number>404256400003</ISI-Number> <Authors> <Author> <DisplayName>Uriol, R</DisplayName> <Person id="rp03449" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Moran, A</DisplayName> <Person id="rp03448" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>International Conference on Control, Automation and Robotics (ICCAR)</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>path planning</Keyword> <Keyword>ant colony optimization</Keyword> <Keyword>mobile robots</Keyword> <Abstract>Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
title Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
spellingShingle Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
Uriol, R
path planning
ant colony optimization
mobile robots
https://purl.org/pe-repo/ocde/ford#4.03.00
title_short Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
title_full Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
title_fullStr Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
title_full_unstemmed Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
title_sort Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
author Uriol, R
author_facet Uriol, R
Moran, A
author_role author
author2 Moran, A
author2_role author
dc.contributor.author.fl_str_mv Uriol, R
Moran, A
dc.subject.none.fl_str_mv path planning
topic path planning
ant colony optimization
mobile robots
https://purl.org/pe-repo/ocde/ford#4.03.00
dc.subject.es_PE.fl_str_mv ant colony optimization
mobile robots
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#4.03.00
description Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/1204
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/ICCAR.2017.7942653
dc.identifier.isi.none.fl_str_mv 404256400003
url https://hdl.handle.net/20.500.12390/1204
https://doi.org/10.1109/ICCAR.2017.7942653
identifier_str_mv 404256400003
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv International Conference on Control, Automation and Robotics (ICCAR)
publisher.none.fl_str_mv International Conference on Control, Automation and Robotics (ICCAR)
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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score 13.413352
Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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