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

Abstract

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 work.