3D path planning of UAV based on improved reinforcement learning

Heng Dan; Hui Peng

doi:10.1117/12.2671556

29 March 2023 3D path planning of UAV based on improved reinforcement learning

Heng Dan, Hui Peng

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 1259405 (2023) https://doi.org/10.1117/12.2671556
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

Aiming at the problem of slow convergence rate of reinforcement learning (RL), an improved RL algorithm based on A* algorithm and velocity obstacle (VO) is proposed. Firstly, a node path obtained by A* algorithm is used to initialize a part of Q table. Then, in the process of path planning by RL, the VO model is applied to output the minimum steering angle required by the obstacle-avoiding flight. The action which is greater than the angle will be taken to interact with the environment. Finally, in order to satisfy UAV performance constraints, B-spline curve approach is applied for path smoothing. The experiment results show that, compared with the standard Q-learning algorithm, the path length of improved RL algorithm is reduced by 26.37%, the success rate of algorithm planning path is increased by 30.76%, and the convergence round is advanced by 88.66%.

Citation Download Citation

Heng Dan and Hui Peng "3D path planning of UAV based on improved reinforcement learning", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 1259405 (29 March 2023); https://doi.org/10.1117/12.2671556

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