吉林大学学报（信息科学版）2024，Vol.42Issue(6)：1025-1030,6.
基于强化学习的无人机航线规划研究
Research on UAV Route Planning Based on Reinforcement Learning
摘要
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.关键词
航线规划/Q-Learning算法/无人机Key words
route planning/Q-Learning algorithm/unmanned aerial vehicle分类
信息技术与安全科学引用本文复制引用
何庆新,涂晓彬,于银辉..基于强化学习的无人机航线规划研究[J].吉林大学学报（信息科学版）,2024,42(6):1025-1030,6.基金项目
福建省科技厅科技计划基金资助项目(2024H0038) （2024H0038）
