Robotic seam tracking system combining convolution filter and deep reinforcement learning

Abstract

To perform automatic, real-time seam tracking tasks effectively, a robust and accurate seam tracking system must be designed. In this paper, we solve the seam tracking issue using a six-axis welding robot, a line laser sensor and an industrial computer. The processing of welding images is the core of the seam tracking system, which aims to determine the weld feature point in each image. We propose a two-stage weld feature point localization method that combines convolution filter and deep reinforcement learning (CF-DRL) to localize the weld feature point in each welding image robustly and accurately. In the first stage, the weld feature point is roughly tracked using a convolution filter tracker. But the position given by the convolution tracker is sometimes not accurate enough due to the natural gap between visual tracking and seam tracking. Consequently, in the second stage, the weld feature point should be further refined using our trained policy network. Using our two-stage weld feature point localization method, the weld feature points can be determined from noisy images in real time during the welding process. The 3D coordinate values of these points are obtained according to the structured light measurement principle to control the movement of the robot and the torch in real time. A robotic seam tracking system is established based on the equipment and methods mentioned above. Experimental results show that the welding torch runs smoothly with a strong arc light and splash interference. The mean tracking error of our experiments reaches 0.189 mm, which can fulfill actual welding requirements. Several comparison tests have been performed to illustrate the robustness and accuracy of our seam tracking system using our welding image dataset.