Real-time image processing for vision-based weld seam tracking in robotic GMAW

Abstract

Image capturing and processing is important in using vision sensor to effectively track the weld seam and control the weld quality in robotic gas metal arc welding (GMAW). Using vision techniques to track weld seam, the key is to acquire clear weld images and process them accurately. In this paper, a method for real-time image capturing and processing is presented for the application in robotic seam tracking. By analyzing the characteristic of robotic GMAW, the real-time weld images are captured clearly by the passive vision sensor. Utilizing the main characteristics of the gray gradient in the weld image, a new improved Canny edge detection algorithm was proposed to detect the edges of weld image and extract the seam and pool characteristic parameters. The image processing precision was further verified by using the random welding experiments. Results showed that the precision range of the image processing can be controlled to be within ±0.3 mm in robotic GMAW, which can meet the requirement of real-time seam tracking.