农机化研究

针对多补给点的多无人机植保作业的航迹规划问题，建立了基于Voronoi图的无人机环境信息模型及作业区域划分，提出了基于粒子群优化(PSO)的航迹规划算法，充分考虑有效作业路径、总路径、总能耗及转弯次数等4个因素，对已知区域进行全覆盖路径规划。仿真结果表明：PSO算法与传统断点续飞方式相比，在有效作业率上提升了2.55%;在总路径、总耗能、综合代价上分别降低了2.55%、3.45%、1.04%。在PSO算法下，植保无人机有效作业率更高，无效路径更短，能量消耗更低，降低了经济成本。

In order to solve the flight path planning problem of multi-replenishing-point multi-UAVs plant protection operation, a UAV environmental information model and operation area division based on Voronoi diagram were established, and a flight path planning algorithm based on particle swarm optimization(PSO) was proposed. Four factors including effective operation path, total path, total energy consumption and turn times were fully considered. Perform full coverage path planning for known areas. The simulation results show that PSO algorithm improves the effective operation rate by 2.55% compared with the traditional breakpoint continuation flight method. The total path, total energy consumption and comprehensive cost are reduced by 2.55%, 3.45% and 1.04% respectively. Under the PSO algorithm, the effective operation rate of plant protection UAV is higher, the invalid path is shorter, the energy consumption is lower, and the economic cost is reduced.