Development of a tool centre point tracker for performance monitoring of industrial robots

Accurate monitoring of robotic Tool Centre Point (TCP) trajectories is essential for ensuring precision, repeatability, and quality in industrial processes such as welding, milling, and assembly. The proposed framework integrates computer vision techniques with trajectory analysis to facilitate the monitoring of robotic operations. The system integrates an industrial robot with an HD camera and Python-based OpenCV processing to visualize and evaluate deviations in real time. The methodology is structured into three stages: (i) contour detection and preprocessing of visual data, (ii) trajectory tracking and visualization, and (iii) accuracy evaluation through nearest-neighbor point cloud analysis and Euclidean distance metrics. The findings demonstrate the system's capacity to reliably detect pointer motion, remove irrelevant background information and compute deviations with millimeter-scale precision. A comparative investigation was conducted to determine the accuracy, consistency, and robustness of automated monitoring in comparison to manual observation.