Research on path planning of mobile robot in complex environment
Abstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmentin...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06713-y |
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| author | Haibin Liu Jingjing Cao Zhiyuan Wang |
| author_facet | Haibin Liu Jingjing Cao Zhiyuan Wang |
| author_sort | Haibin Liu |
| collection | DOAJ |
| description | Abstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments. |
| format | Article |
| id | doaj-art-7a342277629a4e608ff9c68eb38f2159 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-7a342277629a4e608ff9c68eb38f21592025-08-20T03:10:13ZengSpringerDiscover Applied Sciences3004-92612025-04-017411810.1007/s42452-025-06713-yResearch on path planning of mobile robot in complex environmentHaibin Liu0Jingjing Cao1Zhiyuan Wang2Hebei University of EngineeringHebei University of EngineeringHebei University of EngineeringAbstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments.https://doi.org/10.1007/s42452-025-06713-yMove robotImproved algorithmPath planningComplex environment |
| spellingShingle | Haibin Liu Jingjing Cao Zhiyuan Wang Research on path planning of mobile robot in complex environment Discover Applied Sciences Move robot Improved algorithm Path planning Complex environment |
| title | Research on path planning of mobile robot in complex environment |
| title_full | Research on path planning of mobile robot in complex environment |
| title_fullStr | Research on path planning of mobile robot in complex environment |
| title_full_unstemmed | Research on path planning of mobile robot in complex environment |
| title_short | Research on path planning of mobile robot in complex environment |
| title_sort | research on path planning of mobile robot in complex environment |
| topic | Move robot Improved algorithm Path planning Complex environment |
| url | https://doi.org/10.1007/s42452-025-06713-y |
| work_keys_str_mv | AT haibinliu researchonpathplanningofmobilerobotincomplexenvironment AT jingjingcao researchonpathplanningofmobilerobotincomplexenvironment AT zhiyuanwang researchonpathplanningofmobilerobotincomplexenvironment |