Research on AGV Cluster Scheduling Method Based on Parallel Search Algorithm

Abstract

With gradually popularity of the application of AGVs in automated factories, the rational and efficient path planning and scheduling of AGVs can improve the operational efficiency of factories. The search efficiency of the existing A* algorithm is not high and the adaptation of the genetic algorithm remains to be improved. Considering the above problems, this paper proposes a self-adaptive cluster scheduling strategy, adopts the parallel computing to improve the search efficiency of the A* algorithm, and puts forward an improved genetic algorithm based on the dynamic fitness function which does not need to modify the structure of the genetic algorithm according to the changes of the environment so that the adaptability of the algorithm can be improved. Simulation experiments of multi-AGV scheduling are also conducted. The results show that the method adopted in this study has obvious advantages in path solving speed and can adapt to different numbers of obstacles, which provides a reference for the development and application of AGV cluster scheduling methods in real operation scenarios.