In recent years, Unmanned Aerial Vehicles (UAVs) can effectively alleviate the problems of unstable links and low transmission efficiency, which have been applied for Wireless Sensor Networks (WSNs) to speed up data collection and transmission. However, when the multiple UAVs collect data onto the same area, there is a problem of overlapping coverage areas, which will result in low energy efficiency. Therefore, this paper studies the energy-efficient collaborative path planning problem to maximize data collection of UAVs from distributed sensors. Based on built multi-UAVs assisted system for collecting sensors data, we formulate the optimization objective to maximize the data collected by the UAV group within the limits of energy and the total covered area. To solve the problem of UAVs' collaborative path planning, we propose a Hexagonal Area Search (HAS) algorithm, which is combined with multi-agents Deep Q-Network(DQN), called HAS-DQN. By limiting the total coverage of UAVs, HAS-DQN can effectively avoid collision problems with UAVs. Experiments show that HAS-DQN can effectively solve the path overlap problem of multiple UAVs moving at the same cost in an unknown environment.

近年来，无人机（UAV）可以有效缓解链路不稳定和传输效率低的问题，已应用于无线传感器网络（WSN）以加快数据采集和传输。然而，当多架无人机在同一区域采集数据时，会出现覆盖区域重叠的问题，从而导致能源效率低下。因此，本文研究了节能协作路径规划问题，以最大限度地从分布式传感器收集无人机数据。在构建的多无人机辅助传感器数据采集系统的基础上，我们制定优化目标，以在能量和总覆盖区域范围内最大化无人机组采集的数据。解决无人机协同路径规划问题，我们提出了一种六边形区域搜索 (HAS) 算法，该算法与多智能体深度 Q-网络 (DQN) 相结合，称为 HAS-DQN。通过限制无人机的总覆盖范围，HAS-DQN 可以有效避免与无人机的碰撞问题。实验表明，HAS-DQN可以有效解决未知环境下多架无人机以相同代价移动的路径重叠问题。