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Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strat...

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Detalles Bibliográficos
Autores principales: Liu, Lisang, Liang, Jingrun, Guo, Kaiqi, Ke, Chengyang, He, Dongwei, Chen, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204407/
https://www.ncbi.nlm.nih.gov/pubmed/37218768
http://dx.doi.org/10.3390/biomimetics8020182
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author Liu, Lisang
Liang, Jingrun
Guo, Kaiqi
Ke, Chengyang
He, Dongwei
Chen, Jian
author_facet Liu, Lisang
Liang, Jingrun
Guo, Kaiqi
Ke, Chengyang
He, Dongwei
Chen, Jian
author_sort Liu, Lisang
collection PubMed
description Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine–cosine algorithm was used to update the producers’ position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers’ position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment.
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spelling pubmed-102044072023-05-24 Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm Liu, Lisang Liang, Jingrun Guo, Kaiqi Ke, Chengyang He, Dongwei Chen, Jian Biomimetics (Basel) Article Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine–cosine algorithm was used to update the producers’ position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers’ position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment. MDPI 2023-04-27 /pmc/articles/PMC10204407/ /pubmed/37218768 http://dx.doi.org/10.3390/biomimetics8020182 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Lisang
Liang, Jingrun
Guo, Kaiqi
Ke, Chengyang
He, Dongwei
Chen, Jian
Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title_full Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title_fullStr Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title_full_unstemmed Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title_short Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm
title_sort dynamic path planning of mobile robot based on improved sparrow search algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204407/
https://www.ncbi.nlm.nih.gov/pubmed/37218768
http://dx.doi.org/10.3390/biomimetics8020182
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