Interacting Multiple Model Adaptive Unscented Kalman Filter for Accurate Localization of a High-Dynamic Motion Mobile Robot Using Wireless Sensor Network

Abstract

Accurate and precise positioning of a mobile robot is a critical problem in the navigation system, and it has become difficult for localization indoors and in GPS-denied areas. Odometry, which is based on dead reckoning, cannot usually provide long-term accurate position estimates. As a result, integrating encoder-based odometry with Wireless Sensor Network (WSN) measurements is recommended as a good solution for minimizing divergences due to uncertainties. The Kalman filtering technique is used to improve state estimation and measurement fusion. Therefore, this paper describes the design and implementation of an Unscented Kalman Filter (UKF) with an adaptive tuning technique to adjust the process noise covariance matrix, resulting in better position estimation, particularly in high-dynamic motions of the mobile robot. Furthermore, for a mobile robot, this work proposes an Interacting Multiple Model (IMM) method with two dynamic motion models, i.e. Constant Velocity (CV) and Coordinated Turn (CT), to deliver high performance with minimal computational overhead. Several experiments on two types of mobile robots were conducted to evaluate and compare the effectiveness of the proposed localization algorithm. The proposed localization technique outperformed the other three methods and demonstrated its effectiveness in high dynamic motions due to the adaptive tuning system.