This paper proposes a laser sensor-based robotic smart method and introduces the workflow of the method and the key technologies involved, such as point laser locating technology, line laser hand-eye calibration technology. The software framework required for the method of operation is described. The techniques described in this article can avoid the difficulty of robotic manual teaching and improve the smart level of robotic operations and product quality.
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.
Firstly, we build the JARI robot system to research the seam tracking from the image identify. Secondly, principal component analysis (PCA) method based on multivariate statistical analysis is used in feature- level fusion. And it is applied in liver B- image recognition. The recognition results are analyzed and compared. Finally, through the gantry robot 9 degree system to verify the logic of the identify V type seam.
The experimental results show that fusion feature can fully and effectively express seam image, which can bring better recognition results. Analyzing and comparing the feature selection results of different sample images, the results show that feature selection is stable and effective. Comparing with the results of direct PCA fusion applications, the recognition effect after feature selection is better, not only improves the average accuracy rate of recognition but also reduces the time complexity of the recognition process. It has better performance, can be more effectively applicated in welding image recognition.