期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2021
卷号：18
期号：2
页码：1-16
DOI：10.1177/1729881421999937
出版社：SAGE Publications
摘要：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.
