用高效仿真联合优化火星车轮子和控制策略，提升越野性能。

While simulation is vital for optimizing robotic systems, the cost of modeling deformable terrain has long limited its use in full-vehicle studies of off-road autonomous mobility. For example, Discrete Element Method (DEM) simulations are often confined to single-wheel tests, which obscures coupled wheel-vehicle-controller interactions and prevents joint optimization of mechanical design and control. This paper presents a Bayesian optimization framework that co-designs rover wheel geometry and steering controller parameters using high-fidelity, full-vehicle closed-loop simulations on deformable terrain. Using the efficiency and scalability of a continuum-representation model (CRM) for terramechanics, we evaluate candidate designs on trajectories of varying complexity while towing a fixed load. The optimizer tunes wheel parameters (radius, width, and grouser features) and steering PID gains under a multi-objective formulation that balances traversal speed, tracking error, and energy consumption. We compare two strategies: simultaneous co-optimization of wheel and controller parameters versus a sequential approach that decouples mechanical and control design. We analyze trade-offs in performance and computational cost. Across 3,000 full-vehicle simulations, campaigns finish in five to nine days, versus months with the group's earlier DEM-based workflow. Finally, a preliminary hardware study suggests the simulation-optimized wheel designs preserve relative performance trends on the physical rover. Together, these results show that scalable, high-fidelity simulation can enable practical co-optimization of wheel design and control for off-road vehicles on deformable terrain without relying on prohibitively expensive DEM studies. The simulation infrastructure (scripts and models) is released as open source in a public repository to support reproducibility and further research.