Path Planning for Robots Based on Improved Genetic Algorithm
Aiming at the problems of long planning time and path feasibility existing in genetic algorithm (GA) for path planning, an improved method integrating GA with Rapidly-exploring Random Tree (RRT) algorithm is proposed. By generating initial path populations using the RRT algorithm, eliminating redund...
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| Veröffentlicht in: | 2025 10th International Conference on Information Science, Computer Technology and Transportation (ISCTT) S. 211 - 216 |
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13.06.2025
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| Abstract | Aiming at the problems of long planning time and path feasibility existing in genetic algorithm (GA) for path planning, an improved method integrating GA with Rapidly-exploring Random Tree (RRT) algorithm is proposed. By generating initial path populations using the RRT algorithm, eliminating redundant paths with a deletion operator, and introducing an elite selection strategy to optimize the selection operation, the continuity of paths and the convergence speed of the algorithm are effectively improved. Additionally, a path point backtracking method is utilized to further optimize path length and turning points. Experimental results show that the improved algorithm outperforms the traditional A^{*} algorithm and other improved GA in terms of path length, number of inflection points, and iteration times. The path length is shortened by an average of 8%, and the iteration times are reduced to 53 \%-85 \% of those of the comparative algorithms, verifying its high efficiency and robustness in complex environments. |
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| AbstractList | Aiming at the problems of long planning time and path feasibility existing in genetic algorithm (GA) for path planning, an improved method integrating GA with Rapidly-exploring Random Tree (RRT) algorithm is proposed. By generating initial path populations using the RRT algorithm, eliminating redundant paths with a deletion operator, and introducing an elite selection strategy to optimize the selection operation, the continuity of paths and the convergence speed of the algorithm are effectively improved. Additionally, a path point backtracking method is utilized to further optimize path length and turning points. Experimental results show that the improved algorithm outperforms the traditional A^{*} algorithm and other improved GA in terms of path length, number of inflection points, and iteration times. The path length is shortened by an average of 8%, and the iteration times are reduced to 53 \%-85 \% of those of the comparative algorithms, verifying its high efficiency and robustness in complex environments. |
| Author | Ou, Hanwen Zhang, Yongquan Zhang, Tian |
| Author_xml | – sequence: 1 givenname: Yongquan surname: Zhang fullname: Zhang, Yongquan email: 173272062@qq.com organization: Nanchong Vocational and Technical College of Science and Technology,Nanchong,Sichuan,China – sequence: 2 givenname: Hanwen surname: Ou fullname: Ou, Hanwen email: 78654343@qq.com organization: Chongqing University of Arts and Sciences,Yongchuan,Chongqing,China – sequence: 3 givenname: Tian surname: Zhang fullname: Zhang, Tian email: 2447015077@qq.com organization: Nanchong Vocational and Technical College of Science and Technology,Nanchong,Sichuan,China |
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| SubjectTerms | Backtracking Convergence Genetic algorithms Heuristic algorithms improved genetic algorithm mobile robot Path planning Planning rapidly-exploring random tree algorithm Testing Training Transportation Turning |
| Title | Path Planning for Robots Based on Improved Genetic Algorithm |
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