Laser vision seam tracking system based on proximal policy optimization
ISSN: 0143-991x
Article publication date: 19 November 2021
Issue publication date: 1 June 2022
Abstract
Purpose
This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).
Design/methodology/approach
By constructing a neural network based on PPO and using the reference image block and the image block to be detected as the dual-channel input of the network, the method predicts the translation relation between the two images and corrects the location of feature points in the weld image. The localization accuracy estimation network (LAE-Net) is built to update the reference image block during the welding process, which is helpful to reduce the tracking error.
Findings
Off-line simulation results show that the proposed algorithm has strong robustness and performs well on the test set of curved seam images with strong noise. In the welding experiment, the movement of welding torch is stable, the molten material is uniform and smooth and the welding error is small, which can meet the requirements of industrial production.
Originality/value
The idea of image registration is applied to weld seam tracking, and the weld seam tracking network is built on the basis of PPO. In order to further improve the tracking accuracy, the LAE-Net is constructed and the reference images can be updated.
Keywords
Citation
Zou, Y. and Zhou, H. (2022), "Laser vision seam tracking system based on proximal policy optimization", Industrial Robot, Vol. 49 No. 4, pp. 770-778. https://doi.org/10.1108/IR-08-2021-0175
Publisher
:Emerald Publishing Limited
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