Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18227
Title: A localization algorithm for autonomous mobile robots via a fuzzy tuned extended Kalman filter
Authors: Ip, YL
Rad, AB
Wong, YK
Liu, Y
Ren, XM
Keywords: Extended Kalman filter
Fuzzy logic
Fuzzy rule-based scheme
Localization
Issue Date: 2010
Publisher: Taylor & Francis
Source: Advanced robotics, 2010, v. 24, no. 1-2, p. 179-206 How to cite?
Journal: Advanced robotics 
Abstract: The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.
URI: http://hdl.handle.net/10397/18227
ISSN: 0169-1864
EISSN: 1568-5535
DOI: 10.1163/016918609X12586197825736
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