Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles

As of today, autonomous underwater navigation can still be considered a challenging task; the determination of self-localization techniques for autonomous underwater vehicles is an open research topic, and many efforts are undertaken by researchers and companies to improve existing algorithms or to develop new solutions to increase the level of accuracy achievable with current vehicle technologies. In this framework, the research activity carried out during the Ph.D. period concentrated on the study of pose estimation algorithms for mobile robots, with special focus given to the underwater field. Starting from current solutions identified within the state of the art, the work was conducted in parallel on the topics of attitude and position estimation. A nonlinear attitude observer employing inertial and magnetic field data and suitable for use in the underwater field was derived; the estimated attitude constitutes an input for an UKF-based position estimator exploiting position, depth, and velocity measurements. Furthermore, the possibility of including the real-time estimation of sea currents within the developed estimators, relying only on already available measurements, was investigated. The performance of the resulting solutions was evaluated by means of simulations exploiting real navigation data or during suitable experimental test campaigns which allowed to assess their effectiveness in a real-world scenario; the obtained results were satisfying, indicating that the derived algorithms may constitute a valid alternative to existing pose estimation strategies commonly adopted in the underwater field.