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Laser vision seam tracking system based on proximal policy optimization
Industrial Robot ( IF 1.8 ) Pub Date : 2021-11-19 , DOI: 10.1108/ir-08-2021-0175
Yanbiao Zou 1 , Hengchang Zhou 1
Affiliation  

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.



中文翻译:

基于近端策略优化的激光视觉焊缝跟踪系统

目的

本文旨在提出一种基于近端策略优化(PPO)的焊缝跟踪方法。

设计/方法/方法

该方法通过构建基于PPO的神经网络,以参考图像块和待检测图像块作为网络的双通道输入,预测两幅图像之间的平移关系,修正特征点在图像中的位置。焊接图像。建立定位精度估计网络(LAE-Net),用于在焊接过程中更新参考图像块,有助于减少跟踪误差。

发现

离线仿真结果表明,所提算法具有较强的鲁棒性,在具有强噪声的弯缝图像测试集上表现良好。在焊接实验中,焊枪运动平稳,熔料均匀光滑,焊接误差小,能满足工业生产要求。

原创性/价值

将图像配准的思想应用于焊缝跟踪,在PPO的基础上构建了焊缝跟踪网络。为了进一步提高跟踪精度,构建了LAE-Net并可以更新参考图像。

更新日期:2021-11-19
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