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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.
He et al. (Fri,) studied this question.