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- W3144022391 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." @default.
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- W3144022391 date "2021-03-01" @default.
- W3144022391 modified "2023-09-23" @default.
- W3144022391 title "Robot visual measurement and grasping strategy for roughcastings" @default.
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- W3144022391 doi "https://doi.org/10.1177/1729881421999937" @default.
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