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Bayesian Approach with Pre- and Post-Filtering to Handle Data Uncertainty and Inconsistency in Mobile Robot Local Positioning

Bayesian Approach with Pre- and Post-Filtering to Handle Data Uncertainty and Inconsistency in... Abstract One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the robot obtained will be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Systems de Gruyter

Bayesian Approach with Pre- and Post-Filtering to Handle Data Uncertainty and Inconsistency in Mobile Robot Local Positioning

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References (23)

Publisher
de Gruyter
Copyright
Copyright © 2014 by the
ISSN
0334-1860
eISSN
2191-026X
DOI
10.1515/jisys-2013-0078
Publisher site
See Article on Publisher Site

Abstract

Abstract One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the robot obtained will be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors.

Journal

Journal of Intelligent Systemsde Gruyter

Published: Jun 1, 2014

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