Co-Design of Rover Wheels and Control using Bayesian Optimization and Rover-Terrain Simulations
🤖 AI Summary
This work addresses the challenge of jointly optimizing rover wheel design and control strategies on deformable terrain, a task hindered by the prohibitive computational cost of high-fidelity simulations such as the discrete element method (DEM). To overcome this, the authors propose an efficient full-vehicle closed-loop simulation framework based on a continuum-based regolith mechanics (CRM) model, integrated with Bayesian optimization to simultaneously tune wheel geometry parameters and PID gains of the steering controller. The approach balances speed, trajectory tracking error, and energy consumption, achieving the first scalable, high-fidelity co-optimization of wheel and control without reliance on expensive DEM simulations. The accompanying simulation infrastructure is open-sourced to facilitate reproducibility. Over 3,000 simulations, the optimization cycle was reduced from months to 5–9 days, and preliminary hardware experiments confirm that the optimized designs maintain their performance trends on physical platforms.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
rover co-design
deformable terrain
wheel-terrain interaction
autonomous mobility
multi-objective optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian optimization
co-design
rover-terrain simulation
continuum-representation model
multi-objective optimization
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