Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3999 |
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| author | Wenwei Chen Lisang Liu Liwei Zhang Zhihui Lin Jian Chen Dongwei He |
| author_facet | Wenwei Chen Lisang Liu Liwei Zhang Zhihui Lin Jian Chen Dongwei He |
| author_sort | Wenwei Chen |
| collection | DOAJ |
| description | To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm’s ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm. |
| format | Article |
| id | doaj-art-444a4e6daecd4f94a2141cebe10585d6 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-444a4e6daecd4f94a2141cebe10585d62025-08-20T03:08:44ZengMDPI AGApplied Sciences2076-34172025-04-01157399910.3390/app15073999Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window ApproachWenwei Chen0Lisang Liu1Liwei Zhang2Zhihui Lin3Jian Chen4Dongwei He5School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaTo address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm’s ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm.https://www.mdpi.com/2076-3417/15/7/3999grey wolf optimizationchaotic mapconvergence factorevolutionary population dynamicsdynamic weightsdynamic window approach |
| spellingShingle | Wenwei Chen Lisang Liu Liwei Zhang Zhihui Lin Jian Chen Dongwei He Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach Applied Sciences grey wolf optimization chaotic map convergence factor evolutionary population dynamics dynamic weights dynamic window approach |
| title | Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach |
| title_full | Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach |
| title_fullStr | Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach |
| title_full_unstemmed | Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach |
| title_short | Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach |
| title_sort | path planning of mobile robots with an improved grey wolf optimizer and dynamic window approach |
| topic | grey wolf optimization chaotic map convergence factor evolutionary population dynamics dynamic weights dynamic window approach |
| url | https://www.mdpi.com/2076-3417/15/7/3999 |
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