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Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method
For the problem of mobile robot's path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526548/ https://www.ncbi.nlm.nih.gov/pubmed/31198416 http://dx.doi.org/10.1155/2019/1932812 |
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author | Chen, Guoliang Liu, Jie |
author_facet | Chen, Guoliang Liu, Jie |
author_sort | Chen, Guoliang |
collection | PubMed |
description | For the problem of mobile robot's path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability. |
format | Online Article Text |
id | pubmed-6526548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-65265482019-06-13 Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method Chen, Guoliang Liu, Jie Comput Intell Neurosci Research Article For the problem of mobile robot's path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability. Hindawi 2019-05-06 /pmc/articles/PMC6526548/ /pubmed/31198416 http://dx.doi.org/10.1155/2019/1932812 Text en Copyright © 2019 Guoliang Chen and Jie Liu. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Chen, Guoliang Liu, Jie Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title | Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title_full | Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title_fullStr | Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title_full_unstemmed | Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title_short | Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method |
title_sort | mobile robot path planning using ant colony algorithm and improved potential field method |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526548/ https://www.ncbi.nlm.nih.gov/pubmed/31198416 http://dx.doi.org/10.1155/2019/1932812 |
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