Path Planning for Agricultural Robots Based on the Improved Dung Beetle Optimization Algorithm

Abstract

Heuristic algorithms are a commonly used type of algorithm in the path planning of agricultural robots. However, during the iteration process, there may be situations where one gets stuck in a local optimal solution. To solve the above problems, this paper proposes a multi-strategy improved dung beetle optimization algorithm. A raster map environment model is established to plan the path of agricultural robots. In the dung beetle algorithm, Logistics chaotic mapping is first adopted to perform chaotic initialization on Zhongchun, making the population distribution more uniform in the search space and improving the search quality of the population. Secondly, the Levy flight strategy is introduced in the theft behavior to enhance the search ability and diversity of the algorithm and reduce the probability of getting stuck in the local optimal solution. A raster map model for simulating the field working environment was constructed using Matlab. Comparative experiments were conducted among the improved dung beetle algorithm, the original dung beetle algorithm, the sparrow optimization algorithm, and the goose flock optimization algorithm. The results show that the improved dung beetle algorithm has more outstanding performance in terms of path length, number of iterations, and stability.