A CAM-UNet for pose determination of supercapacitor cell module assembly based on monocular vision

• Published in: Industrial robot Vol. 52; no. 3; pp. 343 - 352
• Main Authors: Wu, Zhichao, Shu, Weijing, Song, Limei, Zhu, Xinjun, Yang, Yangang
• Format: Journal Article
• Language: English
• Published: Bedford Emerald Publishing Limited 30.04.2025; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Assembly; Automation; Belt conveyors; Deep learning; Efficiency; Feature extraction; Image processing; Image processing systems; Image segmentation; Localization; Methods; Modules; Monocular vision; Neural networks; Polynomials; Pose estimation; Robotics; Robots; Supercapacitors; Workpieces; Automatic robotic grasping; Monocular vision; Pose estimation; U-Net
• ISSN: 0143-991X; 1758-5791; 1758-5791
• DOI: 10.1108/IR-06-2024-0293

Purpose This paper aims to solve the problems of low stacking efficiency and long production time in the supercapacitor module assembly process, a stacking system based on monocular vision is proposed, including bracket visual positioning, grasping and stacking, and it is applied in actual production. Design/methodology/approach To enhance the robustness of the workpiece location method and improve the location accuracy, the improved U-Net network and image processing algorithms are used to segment the collected images. In addition, for the extracted feature points, the objective function that can be globally optimized is obtained by parameterizing the rotation matrix to construct a polynomial equation system and, finally, the equation system is solved to obtain the final pose estimation, which could improve the accuracy of workpiece location. Findings The result indicates that the proposed method is successfully performed on the manipulator. Besides, this method can well solve the problem of object reflection on the conveyor belt. The Intersection over Union of the image segmentation of the object is 0.9948, and the Pixel Accuracy is 0.9973, which has a high segmentation accuracy for the image. The error range between the method proposed in this paper and the pose estimation is within 2 mm, and the qualified rate of supercapacitor module stacking products is over 99.8%. Originality/value This paper proposes a method of accurately extracting feature points by integrating an improved U-Net network and image processing and uses the workpiece positioning algorithm of the optimal solution PnP problem algorithm. The calculation results show that the algorithm improves the positioning accuracy of the workpiece, realizes the assembly of stacked supercapacitor modules and is applied in industrial production.