一种面向无人机群区域协同覆盖的深度强化学习方法

A Deep Reinforcement Learning Method for Collaborative Coverage of Unmanned Aerial Vehicle Groups

在火星探测任务中,为确保火星车能够获取大范围的地表信息,火星车携带的无人机群需要能够有效覆盖指定区域.本文提出的算法以最大化覆盖区域面积为目标,综合考虑覆盖过程中无人机群通信网络的保持以及能量的高效利用,基于强化学习设计多智能体分布式控制算法完成协同覆盖任务.算法采用CRITIC参数共享机制以及图神经网络,解决模型训练中状态输入的排列不一致问题并且提高模型训练效率.仿真结果表明,本文所提出算法在无人机群覆盖范围、能量消耗和连通性保持等方面,效果优于常见的基线方法.

In the Mars exploration mission,in order to ensure that the rover can obtain large-scale surface information,the drone group carried by the rover needs to be able to effectively cover the designated area.In this paper,a coverage control method is proposed based on multi-agent deep reinforcement learning that aims to maximize the coverage object area and subject to drone communication net and energy efficiency.By adopting the CRITIC parameter sharing mechanism,the training efficiency is improved.The parameter permute invariant property is obtained by utilizing the graph net.The simulation results show that the algorithm proposed in this paper is better than two baseline methods in terms of coverage area,energy efficiency,and connectivity maintenance.