Journal of Jilin University (Information Science Edition) ›› 2024, Vol. 42 ›› Issue (6): 1025-1030.

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Research on UAV Route Planning Based on Reinforcement Learning

HE Qingxin1, TU Xiaobin1, YU Yinhui2    

  1. 1. College of Information Engineering, Minnan University of Science and Technology, Quanzhou 362242, China; 2. College of Communication Engineering, Jilin University, Changchun 130012, China
  • Received:2024-06-26 Online:2024-12-23 Published:2024-12-23

Abstract: The energy consumption of a UAV(Unmanned Aerial Vehicle) determines the length of its operational cycle. To address the issue of low communication-to-energy consumption ratio, a reinforcement learning-based UAV path planning solution is proposed to reduce energy consumption while maintaining high communication quality. The continuous flight space is divided into multi-layer two-dimensional grids to facilitate the generation of UAV state points, and a reward function based on communication quality parameters and energy consumption parameters is established. The Q-Learning algorithm is employed to learn and obtain the path with the optimal communication-to-energy consumption ratio. Experimental results show that the path planned by this learning model can achieve a higher communication-to-energy consumption ratio, demonstrating its practical value.

Key words: route planning, Q-Learning algorithm, unmanned aerial vehicle

CLC Number: 

  • TN929.531