This paper addresses the problem of ice-relative navigation of underwater robotic vehicles for oceanographic missions under moving sea ice. We define the ice-relative navigation problem and report a novel underwater robot vehicle, Nereid-UI, and its navigation system designed for under-ice oceanographic operations in ice-covered seas. The goal of Nereid-UI is to enable deployments in polar ocean regions traditionally considered difficult or impossible to access such as the ice-ocean interface in marginal ice zones, the water column of ice-covered seas, and the water column underlying ice shelves. This paper also reports the navigation results of Nereid-UI's first under-ice sea trials and science operations conducted under moving multi-year sea ice off the ice breaker Polarstern in the Arctic Ocean at 83° N 6° W - about 200 km NE of Greenland.

This paper reports a comparative experimental evaluation of proportional derivative and adaptive model-based control for underwater vehicles. To the best of the authors' knowledge, this is the first such evaluation of model-based adaptive tracking control for underwater vehicles during simultaneous dynamic motion in all 6 degrees-of-freedom. This experimental evaluation revealed the presence of unmodeled thruster dynamics arising during reversals of the vehicle's thrusters, and that the unmodeled thruster dynamics can destabilize parameter adaptation. The three major contributions of this paper are: an experimental analysis of how unmodeled thruster dynamics can destabilize parameter adaptation, a two-step adaptive model-based control algorithm which is robust to the thruster modeling errors present, and a comparative experimental evaluation of adaptive model-based control and proportional derivative control for fully-actuated underwater vehicles preforming simultaneous 6 degree-of-freedom trajectory tracking.

This paper reports a novel adaptive identifier for underwater vehicles as well as its local stability proof and comparative experimental evaluation. The adaptive identification algorithm estimates the hydrodynamic mass, quadratic drag, gravitational force, and buoyancy parameters of a second-order rigid-body plant under the influence of actuator forces and torques. Adaptive model identification methods do not require instrumentation of vehicle acceleration as required of other standard methods, such as conventional least squares. Previous adaptive model identification methods have focused on model-based adaptive tracking controllers however these approaches are not applicable when the plant is either uncontrolled, under open-loop control, or using any control law other than a specific adaptive tracking controller; the adaptive identifier reported herein does not require reference trajectory-tracking, and thus is applicable in these commonly occurring cases. An experimental comparison of adaptive identification and conventional least squares parameter identification is reported. The adaptively identified model is shown to be similar to the least squares identified model.

This paper reports the results of an in-water laboratory evaluation of the state estimation accuracy of a commercially available inertial navigation system (INS), and the effect of variations in the accuracy and the update rate of INS correction data on the accuracy of the INS state estimate. The navigation accuracy of the INS state estimate is evaluated by comparison with navigation data from a highly accurate navigation system available in our laboratory that employs high-frequency acoustic time-of-flight ranging, precision pressure depth sensing, bottom-lock Doppler sonar, and a true-North-seeking gyrocompass that is integral to the INS.