Robot tracking system research basing on optical sensors

Feng Zou; Zhangru Zheng; Kang Zhao; Hongwei Sun

doi:10.1117/12.2502788

27 March 2019 Robot tracking system research basing on optical sensors

Feng Zou, Zhangru Zheng, Kang Zhao, Hongwei Sun

Proceedings Volume 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019; 109702I (2019) https://doi.org/10.1117/12.2502788
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

Abstract

With the continuous development of Computer Vision and a variety of advanced seam imaging equipment, the information contained in the seam image is very rich. It is of great significance for industry automation system. Single image feature is difficult to fully express seam image content. Multi- feature fusion has become a natural way to extract the seam image features. It can comprehensively utilize the seam image information to gain more rapid and accurate understanding of welding images.

From low to high, information fusion can be divided into three levels. The feature-level fusion not only keeps the most original information, but also overcomes the unstable and large characteristics of original data. Fusion feature can be effectively used in seam image recognition.

This paper designs an automatic laser arc hybrid welding system basing on optical sensors. The system has the function of automatic control, which is convenient for setting and adjusting parameters, and the welding attitude control is stable and accurate. To a certain extent, it solves the problems of high requirements and poor adaptability of laser welding system in practical application, and improves the engineering adaptability of laser-arc hybrid welding.

Citation Download Citation

Feng Zou, Zhangru Zheng, Kang Zhao, and Hongwei Sun "Robot tracking system research basing on optical sensors", Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109702I (27 March 2019); https://doi.org/10.1117/12.2502788

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