Autonomous Robotics with Vision-Language AI for Industrial Inspection and Maintenance

Abstract

Industrial environments are rapidly evolving toward Industry 4.0, where autonomous robots and AI enable intelligent inspection and maintenance with minimal human intervention. Traditional inspection methods rely on manual operations, fixed robotic programming, and isolated computer vision techniques that struggle in dynamic industrial environments. This work proposes FACTS, an autonomous industrial inspection and maintenance framework integrating robotics with vision-language AI. The framework combines multimodal sensing, advanced computer vision models (CNNs, Vision Transformers, and vision-language foundation models), and natural language understanding to interpret equipment conditions, detect defects, reason about maintenance requirements, and execute autonomous corrective actions. Vision-language feature fusion enhances contextual understanding, while mathematical optimization improves decision accuracy and computational efficiency. The proposed system supports applications in manufacturing, power plants, aerospace, and critical infrastructure by enabling defect detection, predictive maintenance, safety monitoring, and remote assistance. Experimental evaluation demonstrates significant improvements in inspection accuracy, fault classification, decision-making, maintenance prediction, reduced downtime, and lower human intervention, providing a foundation for next-generation intelligent industrial automation.