A guidance system for robotic welding based on an improved YOLOv5 algorithm with a RealSense depth camera

Abstract

Abstract Vision-based automatic welding guidance technology plays an essential role in robotic welding. A laser vision sensor (LVS) relies on manual intervention to guide the robot when near the workpiece, which reduces the autonomy of the welding robot and productivity. To solve this problem, a robot welding guidance system based on an improved YOLOv5 algorithm with a RealSense Depth Camera was proposed. A coordinate attention (CA) module was embedded in the original YOLOv5 algorithm to improve the accuracy of weld groove detection. The center of the predicted frame of the weld groove in the pixel plane was combined with the depth information acquired by a RealSense depth camera to calculate the actual position of the weld groove. Subsequently, the robot was guided to approach and move over the workpiece. Then, the LVS was used to guide the welding torch installed at the end of the robot to move along the centerline of the weld groove and complete welding tasks. The feasibility of the proposed method was verified by experiments. The maximum error was 2.9 mm in guiding experiments conducted with a distance of 300 mm between the depth camera and the workpiece. The percentage error was within 2% in guidance experiments conducted with distances from 0.3 to 2 m. The system combines the advantages of the depth camera for accurate positioning within a large field and the LVS for high accuracy. Once the position of the weld groove of the workpiece to be welded has been determined, the LVS combined with the robot can easily track the weld groove and realize the welding operation without manual intervention.