Study on automated guided vehicle collision avoidance mechanism with external computer vision

Abstract

In industrial automation, safely operating Automated Guided Vehicles (AGVs) in dynamic environments is challenging. Traditional collision avoidance methods, which rely on technologies like LiDAR (Light Detection and Ranging), have limitations in detection range, accuracy, and cost. This study introduces a new method called the Collision Avoidance System with External Computer Vision (CAECV). CAECV uses two cameras which are equipped on the wall in the factory to detect obstacles, and AGV don’t need to equip any obstacle detection sensors. The detection algorithm, combining YOLOv8 with an attention mechanism, excels at identifying obstacles. The system then converts image coordinates to global coordinates using inverse perspective mapping for precise obstacle localization. Experimental comparisons with traditional TIM laser scanning radar show that CAECV outperforms in detection range, response time, accuracy, and cost. CAECV offers a broader detection range, quicker response, and lower error rates. As more AGVs are deployed, CAECV becomes significantly more cost-effective than LiDAR-based systems. Moreover, CAECV can be integrated with LiDAR systems to enhance AGV obstacle avoidance. CAECV represents a scalable, accurate, and economical advancement in AGV collision avoidance technology for modern industrial use.