Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

Abstract

This paper presents a kinematic extended Kalman filter (EKF) designed to estimate the location of track instantaneous centers of rotation (ICRs) and aid in model-based motion prediction of skid-steer robots. Utilizing an ICR-based kinematic model has resulted in impressive odometry estimates for skid-steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid-steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid-steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of −0.42 m over 40.5 m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000 m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.