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.

翻译：尽管仿真是优化机器人系统的关键手段，但可变形地形建模的高昂成本长期以来限制了其在越野自主移动性全车研究中的应用。例如，离散元法（DEM）仿真通常局限于单轮测试，这模糊了车轮-车辆-控制器之间的耦合相互作用，阻碍了机械设计与控制的联合优化。本文提出了一种贝叶斯优化框架，通过在可变形地形上进行高保真度全车闭环仿真，协同设计火星车车轮几何形状与转向控制器参数。利用基于连续介质表征模型（CRM）的土力学方法在效率与可扩展性方面的优势，我们在牵引固定负载的条件下，通过不同复杂度的轨迹对候选设计方案进行评估。该优化器在多目标框架下（平衡行驶速度、轨迹跟踪误差与能耗）对车轮参数（半径、宽度及轮刺特征）与转向PID增益进行调优。我们比较了两种策略：车轮与控制器参数的同步协同优化，以及将机械设计与控制设计解耦的顺序优化方法。我们分析了性能与计算成本之间的权衡关系。在累计3,000次全车仿真中，优化任务在5至9天内完成，而该团队早期基于DEM的工作流程则需要数月时间。最后，一项初步的硬件研究表明，经仿真优化的车轮设计在物理火星车上保持了相对性能趋势。综上所述，这些结果表明：无需依赖成本过高的DEM研究，可扩展的高保真仿真能够实现可变形地形上越野车辆车轮设计与控制的实用化协同优化。仿真基础设施（脚本与模型）已在公共代码库中开源发布，以支持研究的可复现性与后续探索。