Self-Adaptive Traffic Control Model With Behavior Trees and Reinforcement Learning for AGV in Industry 4.0

Automated guided vehicles (AGVs) are considered as an enabling technology to realize smart manufacturing in the upcoming Industrial 4.0 era. However, several challenges including efficiency, timeliness, and safety still exist in AGVs system in discrete manufacturing shopfloor. To address these challenges, a self-adaptive traffic control model combining behavior trees (BTs) and reinforcement learning (RL) is proposed to implement optimal decisions according to diverse, dynamic and complex situations in Industry 4.0 environments. A cyber-physical systems using multiagent system technology is designed in which components such as AGVs and traffic commander are defined as specific agent that cooperates autonomously with each other. Then, the behavior construction model is constructed by BTs to enumerate all the possible states in AGVs traffic control. An RL model is further developed based on the BTs. By using this approach, in this article, AGVs have the ability to adaptively choose the optimal rule-based strategy from existing optional strategies. The case study of the scenario avoiding collisions at intersections illustrates that the proposed model can enhance self-adaptive capability of AGVs traffic control and simultaneously guarantees efficiency, timeliness, and safety.

Automated summary

By combining behavior trees and reinforcement learning, a self-adaptive traffic control model has been proposed to make optimal decisions for AGV systems in smart manufacturing. The study shows that AGVs can adaptively choose the optimal rule-based strategy, enhancing traffic control capability while ensuring efficiency, timeliness, and safety.