With the development of Industry 4.0, the need for automation of production processes is increasing, with robotics playing a key role. Robotic cells perform tasks with a high degree of precision and speed. In Industry 4.0, there is an increasing emphasis on flexible systems, meaning that robotic operations can change multiple times during operation. Due to the dynamic environment of robotic cells, the need for additional supervision has grown in order to ensure the correct execution of operations. One approach to improving the monitoring of robot performance is the integration of machine vision, which enables real-time monitoring of the assembly process. The goal of the master's thesis was to develop a machine vision system that allows automatic detection of the successful execution of a robotic assembly operation. In this system, the assembly operation is carried out in such a way that the components forming the assembly are not known in advance. The system is designed so that, after initial calibration, it monitors changes in components at the assembly site. If the system detects an error, it communicates this via a communication protocol to the robot’s control system. The system was tested on various robotic assembly systems, as well as on a manual assembly station, thereby validating its functionality.
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