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A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator
The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficien...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255475/ https://www.ncbi.nlm.nih.gov/pubmed/37299899 http://dx.doi.org/10.3390/s23115172 |
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author | Liu, Youyu Tao, Wanbao Li, Shunfang Li, Yi Wang, Qijie |
author_facet | Liu, Youyu Tao, Wanbao Li, Shunfang Li, Yi Wang, Qijie |
author_sort | Liu, Youyu |
collection | PubMed |
description | The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator. |
format | Online Article Text |
id | pubmed-10255475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102554752023-06-10 A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator Liu, Youyu Tao, Wanbao Li, Shunfang Li, Yi Wang, Qijie Sensors (Basel) Article The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator. MDPI 2023-05-29 /pmc/articles/PMC10255475/ /pubmed/37299899 http://dx.doi.org/10.3390/s23115172 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, Youyu Tao, Wanbao Li, Shunfang Li, Yi Wang, Qijie A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title | A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title_full | A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title_fullStr | A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title_full_unstemmed | A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title_short | A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator |
title_sort | path planning method with a bidirectional potential field probabilistic step size rrt for a dual manipulator |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255475/ https://www.ncbi.nlm.nih.gov/pubmed/37299899 http://dx.doi.org/10.3390/s23115172 |
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