Empowering Smartness in Robotics through Digital Twin Design: A Reference Methodology

The production industry has undergone substantial transformations, mainly attributed to economic variables, technological advancements, and the need for improved efficiency. All of these modifications have led to a movement towards more effective and flexible production procedures, where robots play a crucial role in meeting these demands. The use of digital twin technology in robotics has emerged to unlock new levels of smartness, intelligence, adaptability, and efficiency. This paper discusses the development of a standard digital twin methodology for building a robot twin. In order to ensure effective operations in industrial equipment, it is crucial to be able to predict and avoid faults. This is where PHM (Prognostics and Health Management) becomes essential. The PHM approach involves monitoring and predicting the health of devices and systems using data-driven methodologies. However, implementing PHM can be challenging, especially in complex environments such as robotics systems. This is where the proposed digital twin methodology comes in, which provides a comprehensive and adaptable framework for creating virtual replicas of robots and optimizing their performance in real-time. This technique can improve PHM and overall performance by utilizing digital twins and artificial intelligence to increase robots' autonomy, flexibility, and efficiency.