Proposal of hyper-CLS data for industrial robots-HCLS statements for sequence control of multiple robots

Abstract

In designing and manufacturing processes using CAD/CAM systems, cutter location source (CLS) data are generally used for intermediate data to finally generate numerical control (NC) data for various types of NC machine tools. CLS data mainly include statements as ‘GOTO’ to designate the position and orientation of a cutting tool. However, unfortunately, industrial robots and mechatronics systems have not been supported by such standardized CLS data. Also, it has not been realized for CLS data to be able to optionally have special statements for such as handling a customized end-effector and a camera system, executing a visual feedback control or cooperative control, and implementing AI systems like convolutional neural networks (CNNs). This paper aims to propose hyper-CLS data named HCLS data that can support the above functions. The effectiveness of HCLS data is experimentally evaluated by implementing a sequence work process using two small-sized robots with four-DOFs. The cost required for introducing multiple industrial robots is a serious issue for small and medium-sized manufacturers. Recently, there has been a growing need for small-sized industrial robots, which are comparatively easy to be introduced at low cost, in order to automate work processes that have previously relied on human hands, such as parts fitting tasks with a small clearance and cooperative task taking the timing. The proposed cooperative system based on two small-sized four-DOFs articulated robots enables a sequence control for picking and placing without using any programmable logic controller (PLC). The effectiveness and the validity of the proposed robot system are demonstrated through a pick and place experiment.