Robot visual measurement and grasping strategy for roughcastings

International Journal of Advanced Robotic Systems

article

Guoyang Wan¹  Guofeng Wang¹  Kaisheng Xing²  Yunsheng Fan¹  Tinghao Yi³ 

[1] Department of Marine Electrical Engineering, Dalina Maritime University;Xinwu Economic Development Zone, Anhui Institute of Information Technology;University of Science and Technology of China

关键词: Industrial robot;    deep learning;    object detection;    pose measurement;    template matching;   

DOI  :  10.1177/1729881421999937

学科分类：社会科学、人文和艺术（综合）

来源: InTech

【 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.

【 授权许可】

CC BY