On-line Mobile Robot Model Identification using Integrated Perturbative Dynamics

Abstract

We present an approach to the problem of real-time identification of vehicle motion models based on fitting, on a continuous basis, parametrized slip models to observed behavior. Our approach is unique in that we generate parametric models capturing the dynamics of systematic error (i.e. slip) and then predict trajectories for arbitrary inputs on arbitrary terrain. The integrated error dynamics are linearized with respect to the unknown parameters to produce an observer relating errors in predicted slip to errors in the parameters. An Extended Kalman filter is used to identify this model on-line. The filter forms innovations based on residual differences between the motion originally predicted using the present model and the motion ultimately experienced by the vehicle. Our results show that the models converge in a few seconds and they reduce prediction error for even benign maneuvers where errors might be expected to be small already. Results are presented for both a skid-steered and an Ackerman steer vehicle.