Perception-Driven Estimation of Terrain Motion Resistance for UGVs

Abstract

Accurate estimation of wheel–terrain interaction parameters is important for efficient navigation of Unmanned Ground Vehicles in unstructured outdoor environments. In this paper, we propose a hybrid data-driven and model-based method to estimate a priori emph{motion resistance}, a terrain-specific parameter representing the force opposing wheel motion, which is largely influenced by terrain class and geometry. The proposed method relies on learning motion resistance from proprioceptive feedback collected on reference terrains. This learned model is then transferred to new environments, where motion resistance is inferred from exteroceptive observation, including LiDAR and cameras, leveraging terrain geometry and class information. To capture uncertainty from terrain roughness and sensor noise, we evaluate two probabilistic models predicting motion resistance distributions: a Gaussian-MLP and a Gaussian Process Regressor. Their robustness to domain shifts is assessed by measuring performance degradation as the target diverges from the source domain. Extensive off-road field experiments validate the method’s effectiveness, demonstrating accurate prediction of motion resistance and its potential for deployment.