Online fault detection and model adaptation for Underwater Vehicles in the case of thruster failures

Autonomous Underwater Vehicles (AUVs) are required to carry out a mission with minimum supervision. Often, the AUV's hardware integrity is compromised amidst operation; thus, jeopardising the mission's success. Thruster failures, for example, may affect AUVs locomotion. Following a thruster failure, the plan may require changes to compensate, if possible, for the loss of mobility. In this paper, we present an algorithm that identifies thruster failures in run-time. Moreover, the algorithm corrects the vehicle's dynamical model to incorporate the defective thruster. The algorithm uses a Mixture of Gaussians representation for the vehicle's state. Variational Bayes Approximation has been utilised to yield the filtering equations. As indicated by experimental evaluation, the algorithm detects thruster-failure events correctly; and, in turn, learns an accurate dynamical model of the vehicle at its current state. Experiments were carried out on a real platform in a wave tank at Heriot-Watt University.