Robot visual measurement and grasping strategy for roughcastings. (1st April 2021)

• Record Type: Journal Article
• Authors: Wan, Guoyang; Wang, Guofeng; Xing, Kaisheng; Fan, Yunsheng; Yi, Tinghao
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 2(2021)
• Publication Date: 2021-04-01
• Subjects: Industrial robot -- deep learning -- object detection -- pose measurement -- template matching; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881421999937
• Languages: English
• ISSNs: 1729-8806

Abstract

To overcome the challenging problem of visual measurement and grasping of roughcasts, a visual grasping strategy for an industrial robot is designed and implemented on the basis of deep learning and a deformable template matching algorithm. The strategy helps realize the positioning recognition and grasping guidance for a metal blank cast in complex backgrounds under the interference of external light. The proposed strategy has two phases: target detection and target localization. In the target detection stage, a deep learning algorithm is used to recognize the combined features of the surface of an object for a stable recognition of the object in nonstructured environments. In the target localization stage, high-precision positioning of metal casts with an unclear contour is realized by combining the deformable template matching and LINE-MOD algorithms. The experimental results show that the system can accurately provide visual grasping guidance for robots.