摘要
牧羊控制方法逐渐被应用于机场鸟群驱离、无人机放牧、空地协同监视和引导等大规模集群运动协调问题.以牧羊无人机为例,提出基于分层自主决策和深度Q网络(DQN)的自适应牧羊控制方法.首先,考虑离群个体活跃度衰减等因素,建立牧羊控制问题的感知和运动模型;然后,针对个体滞留和离群问题,提出基于全局质心的弧形轨迹(GCM-Arc)控制方法和避障策略,提升羊群受控个体占比;最后,建立分层自主决策模型,结合GCM-Arc控制方法与深度Q网络,提出分层GCM-Arc控制方法,以实现控制模式自适应切换和参数自适应调整.数字仿真实验表明,所提出方法在牧羊任务时间、无人机总路程、羊群平均半径、单体离群率和牧羊任务成功率方面,明显优于经典的两种牧羊控制方法.
The shepherd control method is gradually being applied to address large-scale collective motion coordination problems,such as bird dispersal at airports,drone herding,as well as air-ground coordinated surveillance and guidance.Taking UAV herding as an example,a self-adaptive shepherding control method based on a deep Q-network(DQN)and hierarchical autonomous decision-making is proposed.Firstly,considering the factors such as the decay of the activity of outlying individuals,a perception and motion model of the shepherding control problem is established.Then,a global center of mass arc(GCM-Arc)control method and an obstacle avoidance strategy are proposed to improve the percentage of controlled individuals in the flock for the individual stagnation and outlier problem.Finally,a hierarchical autonomous decision-making model is established,and a hierarchical GCM-Arc control method is proposed by combining the GCM-Arc control method and the DQN,which realizes adaptive switching of control mode and adaptive adjustment of parameters.Simulation experiments demonstrate that the proposed method outperforms classical GCM-V(V-shaped trajectory based on global center of mass)and Arc-Formation shepherding control methods significantly in terms of shepherding task completion time,total drone distance traveled,average radius of the sheep herd,individual outlier rate,and sheep herding task success rate.
作者
赵江
杨智
池沛
王英勋
ZHAO Jiang;YANG Zhi;CHI Pei;WANG Ying-xun(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;Institute of Unmanned System,Beihang University,Beijing 100191,China)
出处
《控制与决策》
北大核心
2025年第5期1523-1532,共10页
Control and Decision
关键词
牧羊控制
无人机
分层自主决策
深度Q网络
自适应
路径规划
shepherding control
unmanned aerial vehicle(UAV)
hierarchical autonomous decision
deep Qnetwork(DQN)
self-adaptive
path planning
