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Low cost automation using INS/GPS data fusion for accurate positioning

Low cost automation using INS/GPS data fusion for accurate positioning Summary Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Robotica Cambridge University Press

Low cost automation using INS/GPS data fusion for accurate positioning

Robotica , Volume 21 (3): 6 – Jun 1, 2003

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References (13)

Publisher
Cambridge University Press
Copyright
Copyright © Cambridge University Press 2003
ISSN
1469-8668
eISSN
0263-5747
DOI
10.1017/S0263574702004757
Publisher site
See Article on Publisher Site

Abstract

Summary Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

Journal

RoboticaCambridge University Press

Published: Jun 1, 2003

Keywords: Adaptive Kalman filtering; Fuzzy logic; Sensor fusion; INS; GPS

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