基于深度分层特征的激光视觉焊缝检测与跟踪系统研究

Research of Laser Vision Seam Detection and Tracking System Based on Depth Hierarchical Feature

针对自适应性低的焊缝跟踪系统在实际焊接环境中易受噪声干扰的问题,结合深度卷积神经网络强大的特征表达能力和自学习功能,研究了基于深度分层特征的焊缝检测和跟踪系统,该系统可精确地从噪声污染的时序图像中确定焊缝位置。为彻底解决焊枪依循计算轨迹运动所出现的抖振问题,设计了模糊免疫自适应的智能跟踪控制算法。实验结果显示,在强烈弧光和飞溅的干扰下,传感器测量频率达20Hz,焊缝跟踪精度约为0.2060mm,且焊接过程中焊枪末端运行平稳。该系统能实现焊缝平滑的实时跟踪,抗干扰能力强,焊缝轨迹跟踪准确,能满足焊接应用要求。

Aimed at the problem that the seam tracking system with low adaptability is sensitive to noise in the actual welding environment, and combined with the strong feature expression ability and self-learning function of the depth convolution neural network, a welding seam detection and tracking system based on depth hierarchical feature is studied. The location of seam from noise-contaminated serial images is accurately determined by this system. A fuzzy immune self-adaptive intelligent tracking control algorithm is designed to completely solve the chattering problem of welding torch following the calculated trajectory. The experimental results show that, under the interference of strong arc and splash, the metrical frequency of sensor can be up to 20 Hz, the tracking accuracy of the welding seam is about 0. 2060 mm, and the end of the welding torch runs smoothly during the process of welding. The system can realize real-time tracking of the welding seam, has strong anti-interference ability, and can accurately track the trajectory of the welding seam, which can meet the requirements of welding application.