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A multi strategy bidirectional RRT* algorithm for efficient mobile robot path planning
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AI
DOI：10.1038/s41598-025-13915-2.png)
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Abstract
En 中文
To address the issues of slow convergence speed and poor path quality of the traditional Rapidly-exploring Random Tree Star (RRT*) algorithm in complex environments, this paper proposes a Multi Strategy Bidirectional RRT* (MS-BI-RRT*) algorithm for efficient mobile robot path planning. In the new node generation phase, an expansion mode scheduling mechanism based on dynamic goal bias probability and expansion feedback is designed to enable adaptive switching among multiple expansion modes, thereby improving expansion efficiency. Meanwhile, a dynamic step size adjustment method based on local obstacle density is introduced to enhance expansion stability. During the parent node rewiring phase, a multi-factor path cost function is constructed to optimize parent node selection, thereby improving path quality. In the post-processing phase, a Bézier curve-based smoothing strategy is employed to improve trajectory continuity and dynamic controllability. Simulation results in five typical environments show that, compared with RRT*, BI-RRT*, APF-RRT*, BI-APF-RRT*, and GB-RRT*, MS-BI-RRT* algorithm reduces the average execution time by 77.50%, decreases the number of nodes by 76.41%, shortens the path length by 4.37%, and achieves a 100% success rate in all environments. These results demonstrate that the proposed method significantly improves convergence speed, path quality, and environmental adaptability, while exhibiting superior robustness.
Keywords:
Path planning
Bidirectional RRT*
Local translational expansion
Artificial potential field
Dynamic step size
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3.9
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28.0W
Citations:
83.5W
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