Proprioceptive Contact State and Contact Point Estimation for a Leg-Wheel Transformable Robot
Kuan-Jung Huang, Wei-Shun Yu, Pei-Chun Lin
AI summary
Problem
Hybrid leg-wheel robots feature complex linkages and continuous rim contacts that invalidate standard point-foot control models, leaving a critical gap in accurately estimating contact state and precise location using only proprioceptive sensors.
Approach
The authors developed a three-stage pipeline that uses a simplified dynamic model and a discrete-time momentum observer to estimate external forces, followed by threshold-based state detection and an optimization algorithm to pinpoint the exact contact location along the wheel rim.
Key results
- A computationally efficient simplified dynamic model abstracting a complex 11-bar leg-wheel mechanism
- A deterministic optimization-based algorithm for precise contact point localization along the rim
- Over 97% single-leg contact state detection accuracy during dynamic trotting gaits
- Contact point localization with an RMS error of 0.0173 m across diverse simulation gaits
Why it matters
This framework enables advanced model-based control for complex hybrid leg-wheel robots by translating their intricate physical interactions into the precise contact abstractions that modern controllers require.
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 informa- tion for this class of robot. The framework is executed on a computationally efficient and simplified dynamic 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 frameworkâs 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.