Fault-Tolerant Control of AGVs via Deep Feature Enhancement and Multi-Source Verification in Complex Industrial Environments

To address the issue of 2D laser-guided automated guided vehicles (AGVs) in industrial intelligent material handling scenarios being susceptible to interference from changes in lighting and complex obstacles, leading to abnormal positioning and mapping and frequent false stops, this paper designs a lightweight, multi-dimensional perception and anti-false-stop YOLOv8 anomaly recognition network, achieving accurate identification of various interferences in complex environments. An adaptive decision-making fault-tolerant control algorithm is proposed, introducing a temporal logic verification and dynamic threshold adjustment mechanism to achieve real-time dynamic switching of obstacle avoidance levels, ensuring efficient coordination between perception decision-making and control execution. An AGV anomaly detection sample set suitable for complex industrial scenarios is constructed, providing reliable data support for model optimization and accuracy evaluation. Finally, real-world deployment verification in a real electronics factory environment shows that this method reduces the vehicle false-stop rate and improves task handling efficiency. This research effectively solves the robust perception problem of AGVs in complex industrial environments and has significant engineering application value.