Obstacle Avoidance for Automated Guided Vehicles in Real-World Workshops Using the Grid Method and Deep Learning

Abstract

An automated guided vehicle (AGV) obstacle avoidance system based on the grid method and deep learning algorithm is proposed, aiming at the complex and dynamic environment in the industrial workshop of a tobacco company. The deep learning object detection is used to detect obstacles in real-time for the AGV, and feasible paths are generated by the grid method, which ultimately finds an AGV obstacle avoidance solution in complex dynamic environments. The experimental results showed that the proposed system can effectively identify and avoid obstacles in a simulated tobacco production workshop environment, resulting in the average obstacle avoidance success rate of 98.67%. The transportation efficiency of cigarette factories is significantly improved with the proposed system, reducing the average execution time of handing tasks by 27.29%. This paper expects to provide a reliable and efficient solution for AGV obstacle avoidance in real-world industrial workshops.