Robot vision, enabled by Deep Learning (DL) breakthroughs, is gaining momentum in the Industry 4.0 digitization process. The present investigation describes a robotic grasp detection application that makes use of a two-finger gripper and an RGB-D camera linked to a collaborative robot. To extract information from an industrial conveyor containing produced components for monitoring, the system leverages a deep convolutional neural network.
The visual recognition system, which is integrated with edge computing units, conducts image recognition for faulty items as well as calculates the position of the robot arm. Identifying deformities in object photos, training and testing the images with a modified version of the You Look Only Once (YOLO) method, and establishing defect borders are all part of the process. Signals are subsequently sent to the robotic manipulator to remove the faulty components. The adopted technique used in this system is trained on custom data and has demonstrated high accuracy and low latency performance as it reached a detection accuracy of 97% for defective pieces.