Abstract
Path planning in complex environments has always been a focus of research for scholars both domestically and internationally. This study addresses the challenge of path planning that combines obstacle avoidance and optimal path searching in scenarios lacking prior knowledge. The proposed approach introduces a parameter dynamic adaptation strategy for path planning. Experimental investigations are conducted using grid-based maps, and the results demonstrate that the method presented in this paper surpasses Q-learning and Sarsa algorithms in terms of comprehensive exploration, enhanced stability, and quicker convergence speed.
This research is supported by National Natural Science Foundation of China under Grant Nos. 62272359 and 62172322; Natural Science Basic Research Program of Shaanxi Province under Grant Nos. 2023JC-XJ-13 and 2022JM-367.
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Yao, G., Zhang, N., Duan, Z., Tian, C. (2024). A Dynamic Parameter Adaptive Path Planning Algorithm. In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14462. Springer, Cham. https://doi.org/10.1007/978-3-031-49614-1_17
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DOI: https://doi.org/10.1007/978-3-031-49614-1_17
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