An FMI-centric open-source and interoperable framework for ai-driven robotic systems: a model-based methodology for integrating physical and AI models

The development of AI-driven cyber-physical robotic systems often depends on proprietary toolchains that limit flexibility and interoperability. This paper presents a modular, open-source, and FMI-centric methodology that integrates OpenModelica-generated Functional Mock-up Units (FMUs), ONNX-based perception pipelines, and ROS 2 middleware into a unified robotic workflow. Beyond software connectivity, the approach makes explicit how perception and control interact through supervisory logic, timing assumptions, and component encapsulation. The methodology is validated on a sign-following mobile robot against an industrial Simulink-based baseline. Both systems achieved 100% task success under nominal conditions. The Simulink baseline was slightly more path-efficient, whereas the FMI-based workflow exhibited substantially lower end-to-end latency. A controlled perception-dropout study further quantified degradation in efficiency, success rate, and latency. A qualitative TurtleBot3 deployment also supports portability beyond simulation. Overall, the results support open, standards-based robotic workflows that are transparent, portable, and reproducible.