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Laser vision seam tracking system based on proximal policy optimization

Yanbiao Zou (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China)
Hengchang Zhou (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 19 November 2021

Issue publication date: 1 June 2022

145

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

Copyright © 2021, Emerald Publishing Limited

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