通信受限条件下多无人机协同环境覆盖路径规划

Coverage path planning for multi UAV collaborative environment under communication constraints

多无人机协同覆盖旨在有效分配多个无人机任务,实现给定区域的快速、高效全覆盖。然而,在现实应用场景中常常因为无人机之间距离超出通信范围,信号传输受阻,导致无人机之间的协作和信息交互面临极大挑战。为此,提出一种基于Deep Q Networks(DQN)的多无人机路径规划方法。采用通信中断率和最大通信中断时间两个指标来评价路径质量,通过构建与指标相关的奖励函数,实现了无人机团队的自主路径决策。仿真实验表明,所提方法在最短路径上可以与传统优化算法效果保持一致,权衡路径下在增加20%路径长度的情况下可以降低80%通信中断率,在全通信路径下则可以实现100%的全过程连接通信,因此可以根据不同的通信环境生成高效覆盖所有环境节点的路径。

The aim of multi-UAV collaborative coverage is to efficiently allocate tasks to multiple UAVs,achieving rapid and effective full coverage of a given area.However,in real-world applications,the distance between UAVs often exceeds the communication range,leading to communication disruptions and challenges in UAV collaboration and information exchange.Therefore,a multi-UAV coverage path planning(CPP)method based on Deep Q Networks(DQN)is proposed.The path quality is evaluated by two indexes,communication disruption rate and maximum communication disruption time,and the autonomous path decision-making for UAV teams is realized by constructing reward functions related to these indexes.Simulation experiments demonstrate that the proposed method can be consistent with the traditional optimization algorithms on the shortest path.Moreover,under a balanced path condition,the communication interruption rate can be reduced by 80%with a 20%increase in path length.Additionally,under full communication path conditions,the communication with connected network throughout the entire process can be achieved by 100%.Therefore,the proposed method can generate efficient paths covering all environmental nodes according to different communication environments.