Abstract
Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.
Highlights
The use of industrial robots in welding is essential for automation or in hazardous and poor working environments
We investigate how many labeled images are required for our semisupervised training strategy and how robust the results are under random selection of labeled images
We found HRNet to perform similar to the Stacked Hourglass method, while the Simple Baseline performed worse
Summary
The use of industrial robots in welding is essential for automation or in hazardous and poor working environments. Various types of optical measurements are used to control the trajectory of the robot path for seam tracking [1]. Can be utilized to recognize and find the position of welding creases to define the weld paths [3]. Visionbased measurement is used for defect detection of weld beads [5]. Structured light sensing for welding seam tracking is one of the widely used techniques in robotic welding [6]. Images captured by the structured light sensors are less affected by the intensity of the lighting in the welding process than passive vision sensors [7]
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