Probabilistic Representation and Its Application of Digital-Twin of Spatio-Temporal Real-World Toward Trustable Cyber-Physical Interactions

The digital twin is a high-precision, real-time representation of the real world. It has evolved from a digital copy of physical objects, such as jet engines, robots, or any machine, to a broader concept to represent a space, room, building, and city, where many objects and humans interact to engage in productive activities. The digital twin has been attracting attention as an important component of cyber-physical systems in many real-world use cases where safety and trustability are critical. In this article, we argue that risk management is dramatically improved through probabilistic representation of the real world whose true status is only inferred with certain uncertainties. We introduce an evolution of the digital twin and then present a probabilistic digital twin of the real world in which the identity and status of any object are represented in a probabilistic graphical structure. Then, we introduce use cases of the probabilistic digital twin, such as probabilistic object recognition, prediction of the existence of obstacles, and risk-sensitive robot control for robotized automation in various environments such as warehouse operations.