Co-Design of Rover Wheels and Control using Bayesian Optimization and Rover-Terrain Simulations
Huzaifa Mustafa Unjhawala, Khizar Shaikh, Luning Bakke, Radu Serban, Dan Negrut
TL;DR
The paper tackles the challenge of co-optimizing rover wheel design and steering control on deformable terrain by integrating full-vehicle, closed-loop Chrono::CRM simulations with Bayesian optimization. It introduces an eight-parameter design space (wheel geometry: $r_o$, $w_r$, $g_r$, $n_g$, $\alpha_g$; steering gains $K_{p,s}$, $K_{i,s}$, $K_{d,s}$) and a multi-objective cost that combines traversal time, path-tracking error, and energy, with the objective defined as $\mathcal{J}(\mathcal{F}(\mathbf{x})) = w_s r_t + w_t e_n + w_p p_n$. The study compares simultaneous joint optimization against a sequential looped approach, showing that the outer wheel radius is the dominant design parameter across scenarios, and that looped optimization substantially reduces computational cost while preserving performance and improving controller identifiability. Generalization to unseen trajectories (racetrack) confirms that optimized designs maintain their relative rankings, albeit with increased tracking error on more challenging paths. The work demonstrates that scalable, high-fidelity simulation can enable practical, vehicle-level co-design for off-road robotics, with open-source code to support reproducibility and extension.
Abstract
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.