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A novel path planning algorithm based on synergistic bidirectional optimization

Published online by Cambridge University Press:  02 January 2026

Shixuan Qi ,
Haofei Lu ,
Ziyi Wang ,
Xin Gao ,
Siyao Chen ,
Yuanjian Liu  and
Weigang Wang Open the ORCID record for Weigang Wang [Opens in a new window]
Shixuan Qi
Affiliation:
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Haofei Lu
Affiliation:
Portland Institute, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Ziyi Wang
Affiliation:
Portland Institute, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Xin Gao
Affiliation:
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Siyao Chen
Affiliation:
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Yuanjian Liu
Affiliation:
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Weigang Wang*
Affiliation:
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
*
Corresponding author: Weigang Wang; Email: wangwg2020@163.com
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Abstract

Path planning, as a critical component of mobile robotic systems, significantly impacts operational efficiency and energy consumption ratios. State-of-the-art algorithms often suffer from inadequate real-time adjustment capability, insufficient dynamic environment adaptation, and suboptimal computational efficiency. To resolve these limitations, we propose a bidirectionally optimized path planning algorithm named Bidirectional Q-learning LPA* (BQ-LPA*), which incorporates three key innovations. Specifically, to enhance the global search capability of the LPA* framework, we replace fixed heuristic functions with a Q-learning-driven adaptive heuristic mechanism, which improves path quality through dynamic heuristic weighting and update strategies. Additionally, to improve the convergence rate and sample efficiency of Q-learning in complex environments, we propose integrating the LPA* framework to provide prior knowledge guidance, which can effectively minimize redundant exploration attempts by informed pathfinding initialization. Moreover, the Q-learning method inherently faces dimensionality challenges in high-dimensional continuous spaces, which manifest as action space congestion, storage bottlenecks, and computational inefficiency. To mitigate these risks, we devise an LPA*-based space discretization strategy that can reduce action space dimensionality and preserve the path feasibility. Experimental results show that, compared with mainstream path planning algorithms, BQ-LPA* achieves higher accuracy and faster convergence in mobile robot path planning.

Keywords

path planningmobile robotQ-learningLPA*bidirectional optimization

Information

Type
Research Article
Information
Robotica , Volume 44 , Issue 1 , January 2026 , pp. 52 - 70
Copyright
© The Author(s), 2026. Published by Cambridge University Press

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