Different from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited beyond a certain time interval. The influence of shooting resolution and blocking of buildings are considered, and the state information of each grid is defined to record the visit information of the ground area. Considering the characteristic of the established model, the multi-constrained cooperative path planning (MCCPP) algorithm is developed. The grids which have not been visited for a long time are received special attentions, and the drone is led to reducing the flight height to cover the gird which has a special requirement on the shooting resolution. The cooperation mechanism among drones is also set to ensure that all the drones can determine the next path point synchronously. An emergency path planning algorithm with the continuous checking strategy is designed for a drone to fly to the specified area and finish a complete coverage of it.

与通常的监视任务（其目标是完全覆盖指定区域）不同，本文针对持续监视任务的无人机协作路径规划问题，考虑了覆盖区域的多个约束条件。目标是最大化无人机的组合覆盖范围，同时优先考虑在特定时间间隔内未访问过的区域。考虑拍摄分辨率和建筑物遮挡的影响，并定义每个网格的状态信息以记录地面区域的访问信息。考虑到所建立模型的特点，开发了多约束协同路径规划（MCCPP）算法。长时间未访问的网格受到特别关注，无人机导致飞行高度降低，以覆盖对射击分辨率有特殊要求的网格。还设置了无人机之间的协作机制，以确保所有无人机都可以同步确定下一个路径点。一种具有连续检查策略的紧急路径规划算法，旨在让无人驾驶飞机飞到指定区域并完全覆盖该区域。