Automated guided vehicle path planning based on an improved grey wolf optimization algorithm
Sen Yang, Jun Liu, Yuchen Yang · 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022) · 2022
In the context of Industry 4.0, AGV trolleys are increasingly being used. An AGV path planning method based on the logistic-tent chaotic grey wolf optimization algorithm is proposed for automatic guided vehicles (AGVs) with reduced population diversity, a tendency to fall into local optimality as well as poor solution accuracy and imbalance between global and local planning during path planning. Adding Logistic-tent chaotic mapping can initialize the initial population diversity of gray wolf individual locations. And changing the convergence factor control strategy of gray wolf optimization algorithm can improve global path optimality and speed up convergence. In this paper simulation results show that the logistic-tent chaotic grey wolf optimization algorithm has better solution accuracy and global search capability, and can quickly plan a better feasible path.