Classification of Digital Twins in Collaborative Robot Modeling Problems

Abstract

The paper explores approaches to classifying digital twins in collaborative robot modeling tasks, taking into account the requirements of the Industry 5.0 concept and cyber-physical systems. A multi-level classification of digital twins is proposed, which includes descriptive, diagnostic, predictive, and prescriptive levels that differ in functionality, mathematical models, and level of decision-making autonomy. The mathematical foundations of each type are analyzed, including state assessment methods, dynamic modeling, and optimization control, and their suitability for monitoring, diagnostics, forecasting, and adaptive control tasks is determined. The results obtained can be used to develop effective digital twins in collaborative robotics and intelligent manufacturing systems.