电讯技术2024，Vol.64Issue(7)：1033-1041,9.DOI:10.20079/j.issn.1001-893x.240422002
基于强化学习的能量受限无人机通信感知轨迹规划方法
A Reinforcement Learning-based Trajectory Planning Method for Energy-constraint UAV Communication and Sensing
摘要
Abstract
For the unmanned aerial vehicle(UAV)trajectory planning problem in UAV-assisted communication and sensing with energy constraints,the authors adopt laser wireless charging to provide additional energy for the UAV,and at the same time consider the constraints of the UAV dynamics and sensing communication rate,and establishe the moving target sensing and communication trajectory planning problem with the goal of maximizing the mutual information of moving target sensing.In order to solve the established optimization problem containing a large number of complex constraints,the original optimization problem is established as a Markov Decision Process,and the processes of UAV motion,energy change,target sensing,and base station communication are modeled as the environment space,and the UAV motor rotation speed is designed as the action space,and a deep reinforcement learning method is used for training to achieve UAV trajectory planning.Due to the consideration of UAV dynamics,the planned trajectory is more in line with the UAV motion characteristics,and the optimal control sequence obtained from the training can be directly applied to the UAV motor,which reduces the difficulty of UAV control.In the designed experimental scenarios,the mutual information of moving target sensing of the proposed method is improved by a factor of about three compared with that of the traditional optimal control method.关键词
无人机/辅助通信感知/轨迹规划/深度强化学习/激光充电/动力学约束Key words
unmanned aerial vehicle(UAV)/assisted communication and sensing/trajectory optimal/deep reinforcement learning/laser charging/dynamic constraints分类
信息技术与安全科学引用本文复制引用
张智琛,何振清,李彬..基于强化学习的能量受限无人机通信感知轨迹规划方法[J].电讯技术,2024,64(7):1033-1041,9.基金项目
中央高校基本科研业务费专项资金资助(YJ202305) （YJ202305）
