Robotic Sorting of Mechanical and Electrical Parts: An Autonomous Vision-Based Approach in a Practical Case Study

In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. The use of robotic systems enhances process accuracy and speed, resulting in significant cost reductions and improved productivity compared to manual labor. This paper aims to design and develop an automated sorting system for various types of mechanical and electrical parts, utilizing image processing and machine vision algorithms with a Delta parallel robot equipped with a two-finger gripper. The target mechanical and electrical parts in this study are screws, nuts, metal washers, rubber washers, retaining rings, rectangular keys, wall plugs, resistors, potentiometers, capacitors, batteries, ICs, and LEDs. The YOLOv3 algorithm and Adaptive Thresholding method are employed to detect and distinguish objects from the background, and size measurement is achieved with the help of a custom marker with known dimensions. In this study, transfer learning based on pre-trained weights of YOLOv3 for the COCO dataset is applied. The proposed system attains a final mean Average Precision ([email protected]) value exceeding 0.95 for part detection using YOLOv3. Additionally, it demonstrates an overall pick-and-place success rate exceeding $\mathbf{9 0 \%}$.