# Vision Systems for a UR5 Cobot on a Quality Control Robotic Station
## 1 Introduction
Meanwhile, for general-purpose systems, the image processing and calibration procedures are performed by the software modules of the vision system in the robot's programming environment through special software equipped with user-friendly interfaces, installed on the robot controller, or installed on an external computer. For instance, Cognex vision URCap packages are software extensions for the Universal Robot system and integrate into PolyScope, the graphical programming interface of Universal Robots. URCap's packages aim to extend any Universal Robot seamlessly with customized functionality [46]. The employment of vision systems enables the detection of product flaws in the initial production stage, while identification of their cause eliminates defective products on the spot. In the realm of industrial automation, vision systems are most commonly used in the verification and quality control of various products, for instance, in the measurement, shape control and sorting of food products [47; 48]; in the control of the surface flatness of welded aluminum bodies [49]; in machine workspace inspection based on ToF technology [50]; in the quality inspection of slate slabs involving the detection of surface defects [51]; in the quality control of clutch friction disks by detecting flaws [52]; in the quality control of bearings by inspecting bearing surfaces and through defect detection  
hardware and software architecture proposed in this paper and the developed algorithms extend existing quality control applications with the process of analysis of moving objects, detection based on shape, and measurement of fundamental geometric quantities in the working space of the UR5 cobot equipped with a vision system consisting of two different and independent cameras based on a PC (implemented in Python 3.12 using OpenCV 4.7.0.68 libraries). In the literature on cobot applications in robotic quality control stations, there is a lack of identical solutions to the presented hardware-software configuration of the proposed UR5 cobot vision system based on a PC architecture and implemented in Python 3.12 using OpenCV 4.7.0.68 libraries. A vision system was developed based on classic image processing and analysis methods, including control and communication modules for the UR5 (Universal Robots, Odense, Denmark), i4-550L robot and LD-90 (OMRON Corporation, Kioto, Japan) autonomous mobile robot. The main contributions of this paper are as follows:  
* The hardware-software architecture of the UR5 cobot vision system incorporated in the quality control station. * The software part of the complete vision system consisting of modules for image sequence acquisitions involving multi-cameras, system calibration (camera and camera-to-robot), image processing and image analysis and modules for remote cobot communication and control. * The expansion of the communication infrastructure offering a wide range of interfaces, enabling integration of the station with other devices/robot stations. * The hardware part of the UR5 cobot vision configuration consisting of two various and independent cameras, one in the eye-in-hand arrangement and the other in the eye-to-hand arrangement. * The UR5 cobot quality control station cooperating with the neighboring stationary i4-550L SCARA robotic station and the LD-90 autonomous mobile robot. The organization of this paper is structured as follows: Section 2 describes the designed and deployed robotic quality control station equipped with the UR5 cobot and an original vision system as a stand-alone station and in cooperation with neighboring stations. Section 3 presents the cobots' vision system encompassing implemented methods and algorithms. An analysis of the results of a series of measurements conducted to determine the level of measurement noise, accuracy, and repeatability of the deployed vision system is presented in Section 4, followed by a conclusion in Section 5.