Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation

Detalhes bibliográficos
Autor(a) principal: Alajlan, Maram
Data de Publicação: 2016
Outros Autores: Chaari, Imen, Koubâa, Anis, Bennaceur, Hachemi, Ammar, Adel, Youssef, Habib
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/9221
Resumo: In this paper, the efficiency of genetic algorithm (GA) approach to address the problem of global path planning for mobile robots in large-scale grid environments is revisited and assessed. First, an efficient GA path planner to find an (or near) optimal path in a grid map is proposed. In particular, large maps instances are considered in this work, as small maps are easy to address by typical linear-time exact algorithms, in contrast to large maps, which require more intensive computations. The operators of the GA planner were carefully designed for optimizing the search process. Also, extensive simulations to evaluate the GA planner are conducted, and its performance is compared to that of the A algorithm considered as benchmarking reference. We found out that the GA planner can find optimal solutions in the same way as A in large grid maps in most cases, but A is faster than the GA. This is because GA is not a constructive path planner and heavily relies on initial population to explore the space of solutions in contrast to A that builds its solution progressively towards the target. It was concluded that, although GA can provide an alternative to A technique, it is likely that they are not efficient enough to beat exact methods such as A when used with a consistent heuristic. The GA planner is integrated in the global path planning modules of the Robot Operating System (ROS), its feasibility is demonstrated, and its performance is compared against the default ROS planner.
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spelling Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and ExperimentationMobile robotsGenetic algorithmsGlobal path planningIn this paper, the efficiency of genetic algorithm (GA) approach to address the problem of global path planning for mobile robots in large-scale grid environments is revisited and assessed. First, an efficient GA path planner to find an (or near) optimal path in a grid map is proposed. In particular, large maps instances are considered in this work, as small maps are easy to address by typical linear-time exact algorithms, in contrast to large maps, which require more intensive computations. The operators of the GA planner were carefully designed for optimizing the search process. Also, extensive simulations to evaluate the GA planner are conducted, and its performance is compared to that of the A algorithm considered as benchmarking reference. We found out that the GA planner can find optimal solutions in the same way as A in large grid maps in most cases, but A is faster than the GA. This is because GA is not a constructive path planner and heavily relies on initial population to explore the space of solutions in contrast to A that builds its solution progressively towards the target. It was concluded that, although GA can provide an alternative to A technique, it is likely that they are not efficient enough to beat exact methods such as A when used with a consistent heuristic. The GA planner is integrated in the global path planning modules of the Robot Operating System (ROS), its feasibility is demonstrated, and its performance is compared against the default ROS planner.ACTA PressRepositório Científico do Instituto Politécnico do PortoAlajlan, MaramChaari, ImenKoubâa, AnisBennaceur, HachemiAmmar, AdelYoussef, Habib20162116-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9221eng1925-709010.2316/Journal.206.2016.6.206-4602metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:50:22Zoai:recipp.ipp.pt:10400.22/9221Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:29:50.466182Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
title Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
spellingShingle Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
Alajlan, Maram
Mobile robots
Genetic algorithms
Global path planning
title_short Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
title_full Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
title_fullStr Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
title_full_unstemmed Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
title_sort Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation
author Alajlan, Maram
author_facet Alajlan, Maram
Chaari, Imen
Koubâa, Anis
Bennaceur, Hachemi
Ammar, Adel
Youssef, Habib
author_role author
author2 Chaari, Imen
Koubâa, Anis
Bennaceur, Hachemi
Ammar, Adel
Youssef, Habib
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Alajlan, Maram
Chaari, Imen
Koubâa, Anis
Bennaceur, Hachemi
Ammar, Adel
Youssef, Habib
dc.subject.por.fl_str_mv Mobile robots
Genetic algorithms
Global path planning
topic Mobile robots
Genetic algorithms
Global path planning
description In this paper, the efficiency of genetic algorithm (GA) approach to address the problem of global path planning for mobile robots in large-scale grid environments is revisited and assessed. First, an efficient GA path planner to find an (or near) optimal path in a grid map is proposed. In particular, large maps instances are considered in this work, as small maps are easy to address by typical linear-time exact algorithms, in contrast to large maps, which require more intensive computations. The operators of the GA planner were carefully designed for optimizing the search process. Also, extensive simulations to evaluate the GA planner are conducted, and its performance is compared to that of the A algorithm considered as benchmarking reference. We found out that the GA planner can find optimal solutions in the same way as A in large grid maps in most cases, but A is faster than the GA. This is because GA is not a constructive path planner and heavily relies on initial population to explore the space of solutions in contrast to A that builds its solution progressively towards the target. It was concluded that, although GA can provide an alternative to A technique, it is likely that they are not efficient enough to beat exact methods such as A when used with a consistent heuristic. The GA planner is integrated in the global path planning modules of the Robot Operating System (ROS), its feasibility is demonstrated, and its performance is compared against the default ROS planner.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-01-01T00:00:00Z
2116-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.language.iso.fl_str_mv eng
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10.2316/Journal.206.2016.6.206-4602
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dc.publisher.none.fl_str_mv ACTA Press
publisher.none.fl_str_mv ACTA Press
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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