多植保无人机协同路径规划

为实现多植保无人机（UAVs）协同作业，并提高作业效率，提出了一种基于改进粒子群优化（PSO）的多植保无人机协同路径规划算法。根据作业区域的形状面积和植保UAV的作业参数划分各架UAV作业区域，采用栅格法生成各区域全覆盖作业航线。以各架植保UAV各架次植保作业距离为算法寻优变量，在确保各架UAV补给时间满足间隔分布约束条件下，综合考虑补给总次数、返航补给总时间、总耗时和最小补给时间间隔4项因素，并构成目标函数，通过采用改进PSO算法，实现了对各UAV返航顺序和返航点位置的寻优。仿真分析结果表明，相较于最大作业距离规划和最小返航距离规划，本文提出的规划算法表现出了较优的性能和较好的作业区域适应性，证实了其有效性和实用性。

Cooperative path planning for multi-sprayer-UAVs

In order to achieve collaborative work and improve operation efficiency of multi-sprayer-Unmanned Aerial Vehicles(UAVs), a cooperative path planning algorithm for multi-sprayer-UAVs based on the improved Particle Swarm Optimization(PSO) is proposed. Considering the working area’s shape and size and the operating parameters of sprayer-UAV, the working area of each UAV is divided. The full coverage route in each area is generated by grid method. The operation distance during one trip of each sprayer-UAV is used as the algorithm optimization variable. Under the condition that the replenishment time of each UAV meets the interval distribution constraint, four factors of replenishment frequency, total replenishment time, total operation time and minimum replenishment interval are comprehensively considered, constituting the objective function. The improved PSO algorithm is applied to optimize the position of return points and return sequence of UAVs. The simulation results show that compared with the maximum operating distance planning and the minimum return distance planning, the proposed planning alqorithm show better performance and better operation area adaptability, which proved its effectiveness and practicability.