Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization
Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-05-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298814211019222 |
| _version_ | 1818863789684031488 |
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| author | Songcan Zhang Jiexin Pu Yanna Si Lifan Sun |
| author_facet | Songcan Zhang Jiexin Pu Yanna Si Lifan Sun |
| author_sort | Songcan Zhang |
| collection | DOAJ |
| description | Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms. |
| first_indexed | 2024-12-19T10:21:21Z |
| format | Article |
| id | doaj.art-9598b16f98974a17b450dd2b23b38898 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-19T10:21:21Z |
| publishDate | 2021-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-9598b16f98974a17b450dd2b23b388982022-12-21T20:26:03ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142021-05-011810.1177/17298814211019222Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimizationSongcan Zhang0Jiexin Pu1Yanna Si2Lifan Sun3 School of Electrical Engineering, Henan University of Science and Technology, Luoyang, China School of Information Engineering, Henan University of Science and Technology, Luoyang, China School of Information Engineering, Henan University of Science and Technology, Luoyang, China School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaPath planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.https://doi.org/10.1177/17298814211019222 |
| spellingShingle | Songcan Zhang Jiexin Pu Yanna Si Lifan Sun Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization International Journal of Advanced Robotic Systems |
| title | Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| title_full | Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| title_fullStr | Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| title_full_unstemmed | Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| title_short | Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| title_sort | path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization |
| url | https://doi.org/10.1177/17298814211019222 |
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