State Estimation of a Robotic Vehicle With Six In-Wheel Drives Using Kalman Filter

摘要

Abstract This paper describes an estimation algorithm for a robotic vehicle with articulated suspension (RVAS) to estimate the vehicle velocity and acceleration states, and the tire forces. The RVAS is an unmanned ground vehicle based on a skid steering using an independent in-wheel motor at each wheel. The estimation algorithm consists of five parts. In the first part, a wheel state estimator estimates the wheel rotational speed and its angular acceleration using Kalman filter, which is used to estimate the longitudinal tire force distribution in the second part. The third part is to estimate respective longitudinal, lateral, and vertical speeds of the vehicle and wheels. Based on these speeds, the slip ratio and slip angle are estimated in the fourth part. In the fifth part, the vertical tire force is then estimated. For a simulation test environment, the RVAS dynamic model is developed using Matlab and Simulink. The RVAS model consists of five main parts which include in-wheel motor model, wheel dynamic model, Fiala tire model, arm dynamic model, and the sprung mass dynamic model. The estimation algorithm is then validated using the vehicle test data and different test scenarios. It is found from simulation results that the proposed estimation algorithm can estimate the vehicle states, longitudinal tire forces, and vertical tire forces efficiently.