Enabling Improved Learning Capability of Industrial Robots with Knowledge Graph Towards Intelligent Digital Twins

Industry 4.0 has transformed the traditional production paradigm, leading to the applications of a range of industrial robotic arms for various purposes and in varied scenarios. The control and cooperation of these robots hold the key to swift reaction to uncertain needs from the market place, for which digital twins has become a promising technology. This paper aims to adding an intelligence dimension to digital twins by enabling learning capability for industrial robots using knowledge graph. Specifically, useful information about the operation scene and processing logic of a robot is collected and converted into graphical knowledge which will be accumulated and updated as a knowledge base for the operation of industrial robots. In our preliminary work, we evaluate the model on a simulation platform and demonstrate the potential of achieving learning capability for the robots through linking the physical world and the cyber world underpinned by knowledge graphs.