Co-Design of Rover Wheels and Control using Bayesian Optimization and Rover-Terrain Simulations

TL;DR

This paper introduces a Bayesian optimization framework for co-designing rover wheels and control systems using high-fidelity simulations, enabling efficient joint optimization for off-road mobility on deformable terrain.

Contribution

It presents a scalable simulation-based co-optimization method for mechanical and control parameters, reducing reliance on costly DEM simulations and improving off-road vehicle design.

Findings

Optimized wheel and control parameters improve traversal speed and energy efficiency.

Simultaneous co-optimization outperforms sequential approaches in performance trade-offs.

The method reduces simulation time from months to days, enabling practical design iterations.

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.