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.

为了有效地执行自动、实时的接缝跟踪任务，必须设计一个强大而准确的接缝跟踪系统。在本文中，我们使用六轴焊接机器人、线激光传感器和工业计算机解决了焊缝跟踪问题。焊接图像的处理是焊缝跟踪系统的核心，旨在确定每幅图像中的焊缝特征点。我们提出了一种两阶段焊接特征点定位方法，该方法结合了卷积滤波器和深度强化学习（CF-DRL）来稳健而准确地定位每个焊接图像中的焊接特征点。在第一阶段，使用卷积滤波器跟踪器粗略跟踪焊接特征点。但是由于视觉跟踪和接缝跟踪之间的自然间隙，卷积跟踪器给出的位置有时不够准确。因此，在第二阶段，应使用我们训练有素的策略网络进一步细化焊接特征点。使用我们的两阶段焊接特征点定位方法，可以在焊接过程中从噪声图像中实时确定焊接特征点。根据结构光测量原理获取这些点的3D坐标值，实时控制机器人和割炬的运动。基于上述设备和方法建立了机器人焊缝跟踪系统。实验结果表明，焊枪运行平稳，具有较强的电弧光和飞溅干扰。我们实验的平均跟踪误差达到0.189 mm，可以满足实际焊接要求。