Laser vision seam tracking system based on image processing and continuous convolution operator tracker. (June 2018) - Zou, Yanbiao; Chen, Tao
Highlights: A set of six-axis robot welding seam tracking experiment platform based on the HALCON machine vision library is designed to resolve the seam tracking issue. A system of laser vision sensor is designed to fulfill the requirements of the visual and detection components. Feature point extraction is investigated by combining the morphological image processing method and continuous convolution operator tracker algorithm to detect seam feature points under the strong arc and splash interference. Abstract: To address the problem of low welding precision caused by the poor real-time tracking performance of common welding robots, a novel seam tracking system with excellent real-time tracking performance and high accuracy is designed based on the morphological image processing method and continuous convolution operator tracker (CCOT) object tracking algorithm. The system consists of a six-axis welding robot, a line laser sensor, and an industrial computer. This work also studies the measurement principle involved in the designed system. Through the CCOT algorithm, the weld feature points are determined in real time from the noise image during the welding process, and the 3D coordinate values of these points are obtained according to the measurement principle to control the movement of the robot and the torch in real time. Experimental results show that the sensor has a frequency of 50 Hz. The welding torch runs smoothly with a strong arc light and splash interference. Tracking error can reach ±0.2 mm, and the minimal distance between the laser stripe and the welding molten pool can reach 15 mm, which can significantly fulfill actual welding requirements.