Evaluating mobile robot navigation behavior in flexible assembly systems through digital twin and real-world experiments

Although digital twins are increasingly used for pre-deployment testing, their reliability as predictive tools remains understudied due to the lack of established validation frameworks. This paper presents a systematic methodology for validating the predictive fidelity of physics-based digital twins in robotic navigation tasks, addressing a critical gap in sim-to-real transferability for industrial mobile manipulators. We propose a novel evaluation approach combining (1) multi-metric comparison (localization accuracy, path consistency, goal accuracy, and navigation performance) between real-world and simulated navigation experiments, and (2) an uncertainty quantification method to establish confidence intervals for digital twin predictions. Using an NVIDIA Isaac Sim model of an omnidirectional mobile manipulator and digitally reconstructed production environments, we conduct 50 real-world and 50 digital twin experiments across five industrial scenarios. The results show a mean Hausdorff distance of 0.195 m between real and simulated paths, localization RMSE differences of 0.005 m, and a path prediction accuracy of ±0.229 m (95% CI). The findings contribute to robotic navigation by addressing key challenges in using digital twins for real-world applications, reducing the sim-to-real gap, and enhancing the reliable deployment of mobile manipulators in flexible assembly systems.