Authors

Abstract

The farmland in the southwestern mountainous areas of China is mostly hilly terrain with multiple obstacles, and traditional manual spraying operations are time-consuming and laborious. The use of agricultural plant protection unmanned aerial vehicle (UAV) can reduce the problem of high manual operation costs. To solve the problem of optimizing the spraying operation path of plant protection UAVs, this study focused on the complex agricultural environment in the southwestern mountainous areas of China. First, a 2D agricultural map model with multiple obstacles was constructed using MATLAB. Second, the optimization requirements for job paths were analyzed, and a path optimization model based on the grid graph method was studied, aiming to shorten the total flight distance and reduce the number of paths. By applying the genetic algorithm, efficient optimization of the spraying path of plant protection UAV was carried out. Simulation verification showed that the optimized path significantly shortened the flight distance, accelerated convergence speed, and effectively avoided local repeated paths, thereby greatly improving the spraying efficiency of plant protection UAV.

Abstract in Chinese

中国西南山区农田多为含多个障碍物的丘陵地貌，传统人工喷洒作业费时费力，借助农业植保无人机可以减少人工作业成本高的问题。为解决植保无人机喷洒作业路径优化问题，本研究针对中国西南山区复杂农田环境，首先利用MATLAB构建了含多障碍物的二维农田地图模型。随后，分析了作业路径优化需求，研究了基于网格图法的路径优化模型，旨在缩短总飞行距离并减少路径数量。通过应用遗传算法，对植保无人机喷洒路径进行了高效寻优。仿真验证显示，优化路径显著缩短了飞行距离，加快了收敛速度，有效避免了局部重复路径，从而大幅提升了植保无人机的喷洒作业效率。