Abstract
Human-robot collaboration (HRC) in manufacturing environments demands robust safety mechanisms that prevent collisions while maintaining productivity. This paper presents a computer vision framework that integrates depth sensing, deep learning-based human pose estimation, and real-time trajectory prediction to enable proactive safety in shared workspaces. The methodology combines a top-view RGB-D camera system with a convolutional neural network (CNN) for human detection and a Kalman filter for motion forecasting. Experimental evaluations in a simulated assembly cell demonstrate that the system achieves a 94.2% detection accuracy for human-robot proximity within 0.5 meters and reduces false alarms by 37% compared to baseline vision-based systems. The framework also incorporates a safety-aware kinodynamic planner that adjusts robot speed based on predicted human motion, resulting in a 28% reduction in unnecessary stoppages. Results indicate that the proposed approach enhances both safety and operational efficiency, aligning with Industry 4.0 requirements for flexible human-robot interaction. The study contributes to the growing body of literature on vision-based safety in HRC by providing a scalable, real-time solution that balances risk mitigation with workflow continuity.
Keywords
human-robot collaboration, computer vision, safety systems, depth sensing, pose estimation, collision avoidance, proactive hazard detection, Industry 4.0