Multiple-Model Based Fault Detection and Diagnosis of Internal Sensors in Mobile Robot. Detection and Diagnosis of Hard/Noise Failures of Sensor via Variable Structure Interacting Multiple-Model Estimator.

Abstract

This paper proposes a multi-model based approach to detection and diagnosis of hard/noise failures of internal sensors in mobile robot. Three system modes (normal mode, hard failure mode, and noise failure modes)of each sensor are modeled. Changes of the three system modes are also modeled as switching from one mode to another in a probabilistic manner. The mode probabilities and sensor outputs are estimated based on a bank of Kalman filters, and they are interacted with each other effectively. To provide better fault decision, the model sets are switched according to the robot motion. The proposed fault detection and diagnosis (FDD) algorithm is formulated based on the variable structure interacting multiple-model (VSIMM) algorithm. The FDD algorithm is incorporated into a dead-reckoning system of our mobile robot with five internal sensors (four wheel-encoders and a yaw-rate gyro). Experimental results show that the FDD algorithm gives the correct fault decision of the sensors and the dead-reckoning system allows the robust self-localization of the robot subject to sensor failures.