Reinforcement learning based path planning in flow fields

Xiang Jin; Wei Lan; Pengyao Yu; Xin Chang

doi:10.1117/12.2646799

7 September 2022 Reinforcement learning based path planning in flow fields

Xiang Jin, Wei Lan, Pengyao Yu, Xin Chang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291R (2022) https://doi.org/10.1117/12.2646799
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

This paper study the path planning problem of autonomous underwater vehicles. We first give a discrete potential field method, which indicates that the global optimal path can be found by searching along the direction of the decreasing potential energy. Then we propose an improved reinforcement learning path planning algorithm, which uses discrete potential energy as the reward function to speed up the convergence of the algorithm and realizes the path planning in the flow field by modifying the reward function. Finally, we demonstrate the effectiveness of the proposed algorithm by simulation experiments.

Citation Download Citation

Xiang Jin, Wei Lan, Pengyao Yu, and Xin Chang "Reinforcement learning based path planning in flow fields", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291R (7 September 2022); https://doi.org/10.1117/12.2646799

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