Multiple-Model Based Fault Detection and Diagnosis of Internal Sensors in Mobile Robot. Detection and Identification of Sensor-Scale Failure via Kalman Filter.

Abstract

This paper proposes a multi-model based approach to fault detection and diagnosis of internal sensors in mobile robot for a robust dead reckoning. A scale failure, at which the scale of sensor outputs differs from the normal, is handled as the failure type. The scale factor of the sensors as well as the robot velocity is estimated with single-model based Kalman filters, each of which is based on a model matching to a failure mode of particular sensors. The model-based estimates of the scale factors are compared with each other, and then fault decision is made. The proposed fault detection and identification algorithm is implemented on our skid-steered mobile robot with five internal sensors (four wheel-encoders and a yaw-rate gyro). Fifteen fault modes are modeled to correctly diagnose the failure of any one of the five sensors. Experimental results show the property of the fault detection and identification algorithm.