A Digital Twin Framework for Uncrewed Systems (UxS): Uncrewed Ground Vehicle (UGV) Use Case

The rapid convergence of Artificial Intelligence, the Internet of Things, and high-capacity Cloud Computing has accelerated the implementation of the Digital Twin (DT) paradigm. However, the practical realization of high-fidelity, interoperable DTs within a Cyber-Physical System (CPS) context remains hindered by architectural deficiencies and the complexity of integrating formal predictive models with the dynamic state of physical assets. This paper addresses these constraints by advancing the previously proposed Digital Twin Enabled Artificial Intelligence Uncrewed System (DEAUS) framework transitioning from theoretical abstraction to utility through a Model-Based Systems Engineering (MBSE) application featuring the Boston Dynamics SPOT as a Uncrewed Ground Vehicle (UGV). By developing a formal system architecture using System Modeling Language (SysML), this work serves as the axiomatic foundation for the Virtual Twin. This high-fidelity digital representation moves beyond traditional “Digital Shadows” by enabling bi-directional, synchronous emulation. A critical advancement in this work is the modeling of low-level system Technical Performance Measures and constraint blocks. This level of precision allows DT to accurately predict stability failure, model actuator wear, and optimize performance in hazardous environments. This foundational framework facilitates the conceptual modularization of complex systems into tractable units while enforcing the Single Source of Truth principle. Furthermore, the architecture embeds digital traceability and data lineage, essential for rigorous Verification and Validation, and for maintaining a forensically sound Chain-of-Custody. Providing an unambiguous blueprint, this domain-agnostic CPS DT solution is designed for replication across diverse UxS platforms, significantly enhancing operational guidance and system resiliency in time-critical mission scenarios such as Disaster Response.