Experimental Validation of a Real-Time Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles

Abstract

This paper presents experimental validation and implementation issues of a model-based sensor fault detection and isolation (FDI) system applied to unmanned ground vehicles (UGVs). Enhanced structural analysis is followed to build the residual generation module, followed by different residual evaluation modules capable of detecting single and multiple sensor faults. The overall proposed sensor fault detection and isolation system (SFDIS) has been tested in real-time on the ATRV-Jr mobile robot when following different trajectories in an outdoors environment. The robot sensor suite includes a Global Positioning System (GPS) antenna, an Inertial Measurement Unit (IMU), and two incremental optical encoders. To increase residual sensitivity to sensor faults, the IMU readings are filtered using a Kalman filter (KF), resulting in a robust and accurate FDI system that detects and isolates single/multiple sensor faults.