Development of a hyper CLS data-based robotic interface for automation of production-line tasks using an articulated robot arm

Cutter location source (CLS) data, which includes goto statements that define position, orientation, and sequence of a cutting tool, are used to generate the final numerical control (NC) data for performing NC machine tool operations. However, most industrial robots and mechatronic systems cannot work with the standardized CLS data, for example, to handle an end-effector or a camera. Moreover, it is not supported by CLS data to have such an extended function as a visual feedback controller or an AI system like convolutional neural networks (CNNs). In this paper, hyper-cutter location source (HCLS) data and its control interface are proposed for a desktop-sized articulated robot to cope with such extended functions for a complete automation in industrial production lines. HCLS data can include extended numerical commands, e.g., for gripper control, selection of joint or linear interpolation, camera snapshot control, estimation of object’s orientation using AI, and visual feedback control to approach to a target object for picking up. The effectiveness and usefulness of the proposed system are demonstrated through pick-and-place experiments using a small 4-DOFs articulated robot named DOBOT Magician.