Robust Dead Reckoning System with Interacting Multiple Model Based Sensor Fault Diagnosis for Mobile Robot

Abstract

An interacting multiple-model (IMM) approach to sensor fault detection and identification (FDI) in the dead reckoning of mobile robots is proposed. Changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and the sensor fault diagnosis and the robot pose estimation are achieved via a bank of parallel Kalman filters. The dead reckoning algorithm is implemented on a skid-steered type of robot, where the thirty-two system modes (one normal mode and thirty-one hard sensor-failure modes) of five sensors (four wheel-encoders and one yaw-rate gyro) are handled. Experimental results validate the effectiveness of the dead reckoning system.