Proprioceptive Contact State and Contact Point Estimation for a Leg-Wheel Transformable Robot

Abstract

Hybrid leg-wheel robots offer exceptional mobility, but their complex mechanics and extended contact surfaces challenge modern control frameworks that rely on simple point-foot models. Accurately estimating both the contact state and the precise contact location using only proprioceptive sensors is a critical and unresolved problem for these platforms. To address this, we present the complete, proprioception-only framework that provides both contact state and contact point information for this class of robot. The framework is executed on a computationally efficient and simplified ynamic model of the complex 11-bar leg mechanism as an example, which enables a discrete-time Generalized Momentum Observer (GMO) to accurately estimate external wrenches. An optimization-based algorithm then precisely localizes the contact point by finding the location along the rim that best explains the full-body dynamics. The framework's performance was validated in high-fidelity simulations across diverse gaits. For contact state validation, the detector demonstrates over 97% single-leg accuracy during a dynamic 0.4 m/s trot. For contact point validation, the localization stage confirms the accurate estimation throughout the stance phase with RMS 0.0173 m. Our work provides the essential contact information required to provide advanced model-based control for these challenging platforms.