Vision algorithm-based robotic arm grasping system is one of the robotic arm systems that can be applied to a wide range of scenarios. It uses algorithms to automatically identify the location of the target and guide the robotic arm to grasp it, which has more flexible features than the teachable robotic arm grasping system. However, for some food packages, their transparent packages or reflective materials bring challenges to the recognition of vision algorithms, and traditional vision algorithms cannot achieve high accuracy for these packages. In addition, in the process of robotic arm grasping, the positioning on the z-axis height still requires manual setting of parameters, which may cause errors. Based on the above two problems, we designed a sorting system for food packaging using deep learning algorithms and structured light 3D reconstruction technology. Using a pre-trained MASK R-CNN model to recognize the class of the object in the image and get its 2D coordinates, then using structured light 3D reconstruction technique to calculate its 3D coordinates, and finally after the coordinate system conversion to guide the robotic arm for grasping. After testing, it is shown that the method can fully automate the recognition and grasping of different kinds of food packages with high accuracy. Using this method, it can help food manufacturers to reduce production costs and improve production efficiency.

翻译：基于视觉算法的机械臂抓取系统是一种可应用于多种场景的机械臂系统。它通过算法自动识别目标位置并引导机械臂进行抓取，相较于示教型机械臂抓取系统具有更灵活的特性。然而，对于部分食品包装而言，其透明包装或反光材质给视觉算法的识别带来了挑战，传统视觉算法难以对这些包装实现高精度识别。此外，在机械臂抓取过程中，z轴高度的定位仍需人工设置参数，这可能导致误差。针对上述两个问题，我们设计了一套采用深度学习算法与结构光三维重建技术的食品包装分拣系统。该系统利用预训练的MASK R-CNN模型识别图像中物体的类别并获取其二维坐标，再通过结构光三维重建技术计算其三维坐标，最后经坐标系转换后引导机械臂进行抓取。经测试表明，该方法能够全自动地以高准确率识别并抓取不同种类的食品包装。采用该方法可帮助食品制造商降低生产成本并提升生产效率。