Smart Autonomous Part Displacement System based on Point Cloud Segmentation

Abstract

Robotic arms are widely used in manufacturing lines to automate the manipulation of products, providing many advantages, such as increasing production and minimizing labour costs. However, most robotic arms operate in a controlled environment, executing predefined movements. Such a feature prevents the robot arm from working in an environment where multiple product types are in different placements. In this way, this concept paper describes the development of a smart robotic system capable of performing an autonomous pick-and-place task of injected moulded parts from the first conveyor belt to the next, based on its spatial data obtained from a 3D scanner. After obtaining the digital point cloud from the moulded part, the PointNet deep learning model was used to segment and then extract the spatial position of its sprue, which is one of the common structures of any moulded part. Finally, the robotic arm combined with its end-effector can pick up these parts regardless of their shape, orientation, and size. The system proposed is composed of three components, i.e., the IRB 1200 robotic arm from ABB, the PhoXi 3D Scanner from Photoneo, and the two-finger gripper PB-0013 from Gimatic. Moreover, all system components were interconnected using Robot Operating System as middleware. This concept paper discusses the setup and plan for the same.