Cart (Loading....) | Create Account
Close category search window
 

Fault detection and identification in a mobile robot using multiple model estimation and neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Goel, P. ; Inst. for Robotics & Intelligent Syst., Univ. of Southern California, Los Angeles, CA, USA ; Dedeoglu, G. ; Roumeliotis, S.I. ; Sukhatme, G.

We propose a method to detect and identify faults in wheeled mobile robots. The idea behind the method is to use adaptive estimation to predict the outcome of several faults, and to learn them collectively as a failure pattern. Models of the system behavior under each type of fault are embedded in multiple parallel Kalman filter (KF) estimators. Each KF is tuned to a particular fault and predicts, using its embedded model, the expected values for the sensor readings. The residual, the difference between the predicted readings (based on certain assumptions for the system model and the sensor models) and the actual sensor readings, is used as an indicator of how well each filter is performing. A backpropagation neural network processes this set of residuals as a pattern and decides which fault has occurred, that is, which filter is better tuned to the correct state of the mobile robot. The technique has been implemented on a physical robot and results from experiments are discussed

Published in:

Robotics and Automation, 2000. Proceedings. ICRA '00. IEEE International Conference on  (Volume:3 )

Date of Conference:

2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.