A Verifiable Service-Oriented Industrial Cyber–Physical Systems Framework for Energy-Aware Autonomous Navigation Using a High-Fidelity Cyber–Physical Twin

Autonomous Cyber–Physical Systems (CPS) must jointly satisfy energy efficiency, accuracy, and real-time constraints, which are typically treated separately in existing methods. This paper proposes a verifiable service-oriented CPS framework for energy-aware autonomous navigation using a high-fidelity cyber–physical twin. The approach integrates physics-based Model Predictive Control (MPC) with explicit power modeling (P=F·v) and Dubins curve-based trajectory generation under the 5C (connection, conversion, cyber, cognition, and configuration) architecture using CARLA for synchronized cyber–physical interaction. The proposed method achieves 30.7% reduction in mean power consumption and 12.5% reduction in total energy usage while maintaining sub-centimeter tracking error (<0.05 m). Mission duration increases by 26.3% with only 7% computational overhead, confirming real-time feasibility. The framework provides a verifiable CPS methodology that unifies physics-based control, digital twin synchronization, and service-oriented design for energy-aware autonomous navigation.