Abstract
The fourth industrial revolution envisages the use of modern smart technologies to automate traditional manufacturing and industrial practices. However, industrial robots execute mostly pre-programmed jobs and are not able to face challenging tasks in unstructured environments. Industry 4.0 pushes for flexibility on target changes and autonomy. In line with the new principles of Industry 4.0, the proposed work describes an autonomous industrial cell that employs several smart technologies for loading jewelry pieces from a conveyor belt to a hooking frame built on purpose. The cell involves an industrial robot, a custom gripper, pneumatic and electric actuators with the aim of moving and opening the frame hooks, and a custom vision pipeline for detecting the feature of interest during the picking and hooking phases. The implemented pipeline makes use of a stereo camera pair mounted under the robot gripper and two fixed monocular cameras. The method employs HOG feature descriptors and machine learning algorithms for the detection. The software architecture is a component-based designed architecture that uses ROS as the underlying framework and ROS-Industrial packages to control the robot. The robot is controlled with position-based commands to reach intermediate positions in the workspace and with velocity command to implement a visual servoing control scheme that runs at 30 Hz and adjusts the robot position with the feedback of the vision during picking and hooking. The proposed visual servoing approach, thanks to the design of the stereo camera and choice of the optics, is able to perceive the features until the final movement phase, differently from most of the visual servoing employed in the literature that, due to the use of RGB-D camera or other vision apparatus, use an open control loop at a standoff distance. The presented work reaches an accuracy of 95% with a cycle time under 8 s.
Published Version
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