Autonomous path generation for side-seal welding of composite plate billets based on binocular vision and lightweight network VGG16-UNet

Abstract

For composite plates side-sealing, traditional teaching-playback method is low-quality and inefficient, and cannot adapt to the rapid development of intelligent manufacturing. Aiming at this problem, an autonomous localization and welding path generation method based on binocular vision and lightweight deep learning network is proposed. Firstly, a lightweight background removal model based on VGG16-UNet (Visual Geometry Group Network-16 U-shaped Network) was proposed to eliminate different interference of illumination and redundant information. Secondly, Hough transform with RANSAC (Random Sample Consensus) correction was employed for accurate line extraction from unsharp workpiece edges. Then, an error compensation strategy was presented. Finally, a positioning accuracy of 0.47 mm was achieved, meeting the requirements for side-sealing. Autonomous localization and welding base path generation for composite plate billets with 20 mm depth grooves at a 3000 mm viewing distance were successfully realized. Welding results demonstrate that the proposed method is accurate and reliable, laying a solid foundation for further autonomous pass planning and adaptive controlling.