An interacting multiple-model (IMM) approach to sensor fault detection and diagnosis (FDD) in dead reckoning is proposed for navigating mobile robots. In this approach, changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and the sensor FDD and state estimate are achieved via a bank of parallel Kalman filters. To provide better FDD performance, mode probability averaging and heuristic decisionmaking logic are combined with the IMM based FDD algorithm. The proposed FDD is implemented on a skid-steered mobile robot, where 32 system modes (one normal mode and 31 hard sensor failure modes) of 5 sensors (4 wheel-encoders and one yaw-rate gyro) are handled. Experimental results validate the effectiveness of the proposed FDD.
IMM-based sensor fault detection and identification for a drive-by-wire vehicle
