Longitudinal Tire Force Estimation of a Robotic Vehicle with Articulated Suspension: An Experimental Model-Based Approach with Localization Using Two GPS Modules

This research introduces a road-tire force estimation algorithm for a robotic vehicle with articulated suspension, leveraging the Kalman filter and dynamic models of the robot's sub-components. The estimation algorithm consists of three stages. Firstly, a wheel states estimator employs the Kalman filter to estimate the wheel's rotational speed and angular acceleration. In the second stage, the wheel torque is estimated using a current sensor, which is then utilized to estimate the longitudinal tire force distribution in the third stage. The results demonstrate the effectiveness of the proposed estimation algorithm in accurately estimating the vehicle states and longitudinal tire forces. Additionally, to facilitate vehicle localization, two GPS devices are employed. Furthermore, a small-scale prototype of the robotic vehicle is fabricated to experimentally verify the estimation algorithm. Indoor and outdoor experiments are conducted to estimate the vehicle states, and tire forces.