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.
2016 IEEE International Conference on Robotics and Automation (ICRA)