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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Bibliographic Details
Published in:2025 10th International Conference on Information Science, Computer Technology and Transportation (ISCTT) pp. 211 - 216
Main Authors: Zhang, Yongquan, Ou, Hanwen, Zhang, Tian
Format: Conference Proceeding
Language:English
Published: IEEE 13.06.2025
Subjects:
Backtracking
Convergence
Genetic algorithms
Heuristic algorithms
improved genetic algorithm
mobile robot
Path planning
Planning
rapidly-exploring random tree algorithm
Testing
Training
Transportation
Turning
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Summary: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.
DOI:10.1109/ISCTT66403.2025.11137968