An Adaptive Unscented Kalman Filter-based Controller for Simultaneous Obstacle Avoidance and Tracking of Wheeled Mobile Robots with Unknown Slipping Parameters

Abstract

A novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problems of a wheeled mobile robot (WMR) with unknown wheeled slipping. The longitudinal and lateral slipping are processed as three time-varying parameters and an Adaptive Unscented Kalman Filter (AUKF) is designed to estimate the slipping parameters online More specifically, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the Unscented Kalman Filter (UKF) context. A stable unified controller is applied to simultaneously handle tracking and obstacle avoidance for this WMR system to compensate for the unknown slipping effect. Applying Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotically convergent regardless of unknown slipping, the tracking errors converge to the zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated through simulation and experimental results.