Enhancing Manufacturing Efficiency With Computer Vision And Doosan Robotics: An Overview Of Real-Time Object Detection And Automated Decision-Making

This article presents a solution developed with Doosan Robotics and OnRobot for identifying missing or broken pull tabs in manufacturing processes using computer vision and robotics integration. The solution involves a collaborative robot equipped with a 2.5D camera and an end-effector capable of grasping and manipulating objects. The 2.5D camera captures images of the objects, and computer vision algorithms are applied to detect and identify the presence of pull tabs and assess their condition. The system utilizes advanced image processing techniques to analyze the captured images, identifying any instances of missing or broken pull tabs. Upon detection, the robot employs its end-effector to interact with the identified pull tabs, facilitating their removal or replacement as necessary. The integration also incorporates automated decision-making algorithms to optimize production efficiency, reduce errors, and improve product quality. Integrating these technologies enhances precision, accuracy, and speed in identifying and manipulating objects, leading to increased productivity, cost reduction, and improved product quality.