Computational environment specifications.

This paper presents a novel hybrid simulation framework by combining nonlinear model predictive control (NMPC) and the lattice Boltzmann method (LBM) for autonomous robot navigation in dynamic environments. We evaluated the control algorithm’s resilience by looking at fluid-structure interactions in both laminar (Re = 100) and turbulent (Re = 2000) flows. To ensure numerical accuracy and physical fidelity, a systematic grid independence study was conducted across various resolutions ( to ). The grid was selected as the benchmark standard, providing 3–5 lattice units within the viscous boundary layer to minimize numerical diffusion and accurately resolve high-frequency vortex shedding patterns. This rigorous validation allowed us to test the NMPC trajectory planning across fundamentally different flow behaviors with high confidence in the underlying hydrodynamics. The aim is to enhance mobile robot navigation by integrating a resilient control algorithm with a comprehensive fluid dynamics study, focusing on enhancing trajectory planning, obstacle avoidance, and overall performance in dynamic fluid environments. Computational fluid dynamics (CFD) analysis is combined with a robust control algorithm. The robot’s interaction with the surrounding fluid is evaluated through different parameters such as Reynolds number, drag forces, the robot’s energy dissipation, and vorticity. Key performance metrics, including a path efficiency of 0.887 and low computational requirements with the LBM-NMPC framework maintaining a linear memory footprint of 2.88 MB at peak resolution, demonstrate NMPC algorithm’s viability as a fast and efficient trajectory planner. The robot maintained safe distances from obstacles, highlighting the effectiveness of the obstacle avoidance strategy and the robustness of the validated simulation environment.