Trajectory Optimization Method for Automatic Feeding Vehicles Based on the Grey Wolf Algorithm
Abstract：To address the issue of low efficiency and tendency to fall into local optima when using swarm intelligence algorithms for path planning of automatic feeding vehicles, an improved grey wolf algorithm is proposed to solve the path planning problem of feeding vehicles in complex environments. First, a differential strategy is used to achieve mutation of population individuals. Then, a crossover probability factor is utilized to generate new experimental individuals, enhancing the diversity of the population. Finally, three optimal individuals are selected as alpha wolves to update the current position of the grey wolf. After establishing the kinematic and dynamic models of the feeding vehicle, both the traditional grey wolf algorithm and the improved grey wolf algorithm are used to solve the optimal path of the feeding vehicle under constraints. Experimental results show that the improved grey wolf algorithm reduces the average solving time by 34.8% and decreases the probability of falling into a local optimum by 86.9%, verifying the effectiveness of the algorithm in path planning for automatic feeding vehicles.
Keywords：Feeding Vehicle Trajectory OptimizationGrey Wolf AlgorithmDifferential EvolutionConstraints
Publication Date：2025-06-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 27-32 )