Tire Force Estimation of Dynamic Wheeled Mobile Robots using Tire-Model Based Constrained Kalman Filtering

Abstract

We propose a novel real-time algorithm to estimate the full three-dimensional individual tire forces (i.e., vertical, longitudinal as well as lateral) of a car-like rearwheel-driven four wheel wheeled mobile robots equipped with onboard navigation sensors and wheel encoders. The key enabling idea for this is to utilize the tire model (i.e., the magic formula) in a feedback manner on the framework of the constrained Kalman filtering to render the tire force estimation: 1) more accurate as compared to the typical tire force estimation techniques neglecting the tire-road interaction; and 2) more robust as compared to the results adopting the tire model, yet, only in an open-loop manner. Our proposed algorithm, while performing this full tire force onboard/real-time estimation, also provides the estimation of: 1) tire-road friction coefficient; and 2) torque inputs of the rear left and right wheels, which are connected via differential gear. Simulations with CarSim and outdoor experiments are performed to validate the proposed estimation algorithm.