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
何庆新 1涂晓彬 1于银辉2
作者信息
- 1. 闽南理工学院信息工程学院,福建泉州 362242
- 2. 吉林大学通信工程学院,长春 130012
- 折叠
摘要
为解决无人机的低通信能耗比问题,并在维持高通信质量的同时降低能耗,提出了一种基于强化学习的无人机航线规划方案.将连续的飞行空间划分为多层二维网格以便于生成无人机状态点,并建立一个基于通信质量和能耗参数的奖励函数,通过Q-Learning算法学习获得通信能耗比最优航线.实验结果表明,该学习模型规划的航线能获得较高的通信能耗比,具有一定应用价值.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据