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Fault Diagnosis and Reconfiguration for Mobile Robot Localization Based on Multi-Sensors Data Fusion

In this paper, we propose a new approach for fault tolerant localization using multi-sensors data fusion for a unicycle-type mobile robot. The main contribution of this paper is a new architecture proposal for fault diagnosis and reconfiguration for mobile robot localization using multi-sensors data fusion and the duplication/comparison approach. Four different sensors usually embedded in mobile robots (Camera, IMU, GPS, and Odometer) are considered, while six different sensors couples combinations are used for sensor data fusion and the duplication of the localization and estimation system. In order to reach this aim, three different filters (EKF, SVSF, and ASVSF) have been proposed and compared. For each selected filter, a comparison mechanism is then introduced to compute different residuals by comparing the estimated robot position for each sensor couples separately. Faults are then detected using the structural residual diagnosis method. This approach assumes the occurrence of a single fault at a given time. A reconfiguration mechanism is then applied by selected the healthy sensors couple and their corresponding fusion filter. Several scenarios are considered for navigation-based fault tolerant localization approaches. Simulation results are presented to illustrate the advantage and performance of the proposed architecture. The proposed solutions are implemented and validated successfully using the V-REP simulator.