Multi-area coverage path planning for plant protection UAVs based on a hybrid strategy beluga whale optimization algorithm
Zhiping Tan, Kaicheng Huang, Yu Tang, Mingwei Fang, Huasheng Huang · Smart Agricultural Technology · 2025
Traditional path planning algorithms often suffer from high computational cost, slow convergence, and local optima entrapment, limiting their practical use. The beluga whale optimization algorithm (BWO), with its simple mathematical model and robust global search capability, provides an efficient approach for unmanned aerial vehicle (UAV) coverage path planning. To address the requirements of plant protection UAVs operating across multiple farmland areas, this paper proposes a multi-area coverage path planning method based on a hybrid strategy beluga whale optimization algorithm (HSBWO). In HSBWO, a circle chaotic mapping technique is introduced to enhance population initialization, thereby increasing diversity. An elite strategy further guides individuals toward more effective global exploration, improving the algorithm's ability to escape local optima. During the exploitation phase, an adaptive Lévy flight step sizes and a spiral search strategy are incorporated to further enhance optimization performance. The algorithm is applied to multi-area coverage path planning for plant protection UAVs, with experiments conducted on simulated maps and compared against various heuristic algorithms. Experimental results demonstrate that the proposed method generates coverage paths that significantly reduce both energy and pesticide consumption. Specifically, total energy consumption is reduced by up to 6.46%, and redundant coverage is decreased by up to 1.39% compared to other algorithms.