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.

为了克服视觉测量和粗糙物体抓取的难题，在深度学习和可变形模板匹配算法的基础上，设计并实现了工业机器人的视觉抓取策略。该策略有助于在外部光线的干扰下，实现复杂背景下的金属毛坯的定位识别和抓握指导。所提出的策略分为两个阶段：目标检测和目标定位。在目标检测阶段，深度学习算法用于识别物体表面的组合特征，以便在非结构化环境中稳定地识别物体。在目标本地化阶段，通过结合可变形模板匹配和LINE-MOD算法，可以实现轮廓不清晰的金属铸件的高精度定位。实验结果表明，该系统可以准确地为机器人提供视觉抓取指导。