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Machine learning

Efficient Mobile Robot Path Planning with Multi-Strategy Bidirectional RRT* Algorithm

Strategic Insights

— 1 min read

A new study on Robotics has shed light on the limitations of traditional Rapidly-exploring Random Tree Star (RRT*) algorithm in complex environments. Researchers from Anhui University of Science and Technology have proposed a Multi Strategy Bidirectional RRT* (MS-BI-RRT*) algorithm to address the issues of slow convergence speed and poor path quality. The MS-BI-RRT* algorithm is designed to enable adaptive switching among multiple expansion modes, thereby improving expansion efficiency and enhancing expansion stability. Simulation results demonstrate that the proposed method significantly improves convergence speed, path quality, and environmental adaptability, while exhibiting superior robustness.

Key Takeaways:

  • The MS-BI-RRT* algorithm proposes an expansion mode scheduling mechanism based on dynamic goal bias probability and expansion feedback to enable adaptive switching among multiple expansion modes.
  • The algorithm introduces a dynamic step size adjustment method based on local obstacle density 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.
  • A Bezier curve-based smoothing strategy is employed to improve trajectory continuity and dynamic controllability in the post-processing phase.
  • Simulation results 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.
  • The proposed method demonstrates superior convergence speed, path quality, and environmental adaptability, while exhibiting robustness.

Statistics:

  • The MS-BI-RRT* algorithm reduces the average execution time by 77.50% compared to other algorithms.
  • The algorithm decreases the number of nodes by 76.41%, resulting in improved memory efficiency.
  • The path length is shortened by 4.37%, making the algorithm more efficient in complex environments.
  • The MS-BI-RRT* algorithm achieves a 100% success rate in all environments, demonstrating its robustness and adaptability.

Sources:

  • A multi strategy bidirectional RRT* algorithm for efficient mobile robot path planning. Scientific Reports, 2025;15(1):29501.
  • NewsRx. Studies from Anhui University of Science and Technology in the Area of Robotics Reported (A multi strategy bidirectional RRT* algorithm for efficient mobile robot path planning). Journal of Engineering. August 25, 2025; p 3462.

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