基于激光视觉的薄板焊缝跟踪方法研究

由于示教型焊接机器人在进行汽车薄板件连续焊工艺时存在装夹误差和热变形等问题,导致焊缝实际轨迹与示教轨迹存在较大误差。为提高焊接质量,基于焊接机器人构建激光视觉焊缝检测跟踪系统,提出基于目标估计准则的焊缝跟踪算法,实时跟踪焊缝中心点三维位置变化。以传统图像处理法提取初始帧焊缝特征点,通过改进的孪生神经网络对强干扰下的焊缝特征点进行跟踪提取。通过坐标转换得到机器人基坐标系下的焊缝中心特征点三维坐标。结果表明:该算法能精确提取跟踪焊缝特征点,平均误差为0.48 mm,平均帧率为90帧/s,优于传统图像处理方法和基于相关滤波的方法,能够实现快速准确的跟踪。

Research on Tracking Method of Thin Plate Weld Seam Based on Laser Vision

When teaching welding robot performs the continuous welding process of automobile thin plate parts,due to the problems of clamping error and thermal deformation,the error between actual trajectory and teaching trajectory of the welding seam is large.In order to improve the welding quality,a laser vision weld detection and tracking system was constructed based on a welding robot,and a weld seam tracking algorithm based on target estimation guideline was proposed to track the three-dimensional position change of the weld center.The initial frame weld feature points were extracted by using traditional image processing method,and the improved siamese network was used to track and extract the welding seam feature point under strong interference.The three-dimensional coordinates of the welding seam center feature points in the robot base coordinate system were obtained through coordinate conversion.The results show that the by using the algorithm,the welding seam feature point can be accurately extracted and tracked,with an average error of 0.48 mm and an average frame rate of 90 f/s,which is better than traditional image processing methods and methods based on correlation filtering,and fast and accurate tracking can be achieved.