S. I., Sukhatme G.S., and Bekey G.A.:
Detection and Identification in a Mobile Robot using Multiple-Model Estimation"
This paper introduces a method
to detect and identify faults in wheeled mobile robots. The idea behind the method
is to use adaptive estimation to predict (in parallel) the outcome of several
faults. Models of the system behavior under each type of fault are embedded in
the various parallel estimators (each of which is a Kalman Filter). Each filter
is thus tuned to a particular fault. Using its embedded model each filter predicts
values for the sensor readings. The residual (the difference between the predicted
and actual sensor reading) is an indicator of how well the filter is performing.
A fault detection and identification module is responsible for processing the
residual to decide which fault has occurred. As an example the method is implemented
successfully on a Pioneer I robot. The paper concludes with a discussion of future