A binary gridding path-planning method for plant-protecting UAVs on irregular fields

Abstract

The use of plant-protecting unmanned aerial vehicles (UAVs) for pesticide spraying is an essential operation in modern agriculture. The balance between reducing pesticide consumption and energy consumption is a significant focus of current research in the path-planning of plant-protecting UAVs. In this study, we proposed a binarization multi-objective model for the irregular field area, specifically an improved non-dominated sorting genetic algorithm–II based on the knee point and plane measurement (KPPM-NSGA-ii). The binarization multi-objective model is applied to convex polygons, concave polygons and fields with complex terrain. The experiments demonstrated that the proposed KPPM-NSGA-ii can obtain better results than the unplanned path method whether the optimization of pesticide consumption or energy consumption is preferred. Hence, the proposed algorithm can save energy and pesticide usage and improve the efficiency in practical applications.