Terminal iterative learning control for calibrating systematic odometry errors in mobile robots

Abstract

Odometry calibration is an essential step for successful navigation of any moving system because most of the control algorithms work based on odometry information. Odometry errors can be categorized as systematic and non-systematic errors. In this paper, a terminal iterative learning control (TILC) based odometry calibration method is proposed to calibrate the systematic errors. First, a TILC algorithm for discrete-time nonlinear systems is derived. The sufficient condition for the existence of this control algorithm is also determined. Then, using the TILC, an odometry calibration method is developed. The main advantage of this technique is that there is no need to follow any predefined trajectory instead any arbitrary trajectory can be chosen to calibrate the systematic errors. By simulation and experiment results, the efficacies of both the learning algorithm as well as the calibration technique are evaluated and the results show that the proposed method reduces odometry error significantly.