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The rise of Industry 4.0 has accelerated the adoption of intelligent automation in high-throughput manufacturing environments. Automated guided vehicles (AGVs) rely heavily on magnetic guidance tracks, which are susceptible to wear, contamination, and structural degradation. These defects frequently cause AGV misalignment, emergency stops, and production downtime. This paper presents a lightweight, embedded, vision-based framework for real-time monitoring of AGV magnetic tracks using Raspberry Pi 4 cameras and Python-based computer vision algorithms. The system integrates grayscale intensity modeling, histogram-based MeanShift tracking, contour continuity analysis, and machine learning-assisted classification to detect missing segments, wear, and foreign object interference. Experimental validation on a 30 m test track and five years of industrial data (>3000 samples) demonstrate robust tracking, reliable anomaly detection, and zero false positives under nominal conditions. The proposed hybrid deterministic, ML architecture supports predictive maintenance, reduces downtime risk, and contributes to resilient Industry 4.0 material-handling systems.
Botomba et al. (Wed,) studied this question.