Developing an Inspection Experimental System Integrating of Robot Sorting

Research and development of a set of integrated robot sorting inspection experimental system, the system in industrial automation, quality control, safety monitoring and other fields has a wide range of application prospects. The technical method uses a GPU-enabled TensorFlow Docker image, based on the Mask R-CNN (Mask Region-based Convolutional Neural Network) network architecture, uses the COCO (Common Objects in Context) weight file as the pre-trained deep learning model, and uses VIA (VGG Image Annotator) to label the dataset of the part of end cap of the reducer. The actual experimental results show that: the UR10 Robot is driven to pick up and handle the a single end cap part in the stacked parts to the designated location, the UR10e Robot, with on the MetraSCAN 3D scanner, is equipped with a C-Track tracker and realizes automatic inspection experimental through a programmed program. The experimental teaching application effect has also been well feedback. The research results not only provide a good research foundation for the development and application of system-related technologies, especially measurement or robotics, but also provide a reference for the integration of software and hardware resources in the laboratory.