Ocean Worlds represent one of the best chances for extra-terrestrial life in our solar system. A new mission concept must be developed to explore these oceans. This mission would require traversing the 10s of km thick icy shell and releasing a submersible into the ocean below. During the transit of the icy shell and the exploration of the ocean, the vehicle(s) would be out of contact with Earth for weeks or potentially months at a time. During this time the vehicle must have sufficient autonomy to locate and study scientific targets of interest. One such target of interest is hydrothermal venting. We have previously developed an autonomous nested search method to locate and investigate sources of hydrothermal venting by locating local maxima in hydrothermal vent emissions. In this work we demonstrate this approach on board an OceanServer Iver2 AUV in Chesapeake Bay, MD using simulated sensor data from a hydrothermal plume model. This represents the first step towards the deployment of this approach in conditions analogous to those that we might expect on an Ocean World.

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.

Survey class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precise navigation near the seafloor. In cases where the distance to the seafloor is greater than the DVL bottom lock range, localizing between the surface where GPS is available and the seafloor presents a localization problem, since both GPS and DVL are unavailable in the mid-water column. Previous work proposed a solution to navigation in the mid-water column that exploits the stability of the vertical water current profile in space over the minutes scale. With repeated measurements of these currents with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during vertical descent, along with sensor fusion of other low cost sensors, position error growth is constrained to near the initial velocity uncertainty of the vehicle at the sea surface during the dive. Following DVL bottom lock, due to correlations in the joint vehicle and water current velocity estimation, the entire velocity history is further constrained. In this paper a number of extensions are developed to improve navigation performance during missions characterized by prolonged time-scales and horizontal transits. This includes explicitly accounting for the temporal evolution of water currents while revisiting locations and during the re-observation itself, in addition to employing high fidelity spatial models to account for the horizontal water current field. Furthermore, the validity of our method is demonstrated on deep water data sets. Results from the Sentry AUV are presented from a hydrothermal vent flux estimation mission and a surveying mission at Deepwater Horizon, illustrating a real-time dead-reckoning method with results for up to 20 hours.

We report a comparative performance evaluation, using at-sea field data, of recently reported methods for the problem of in-situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and inertial navigation sensors arising in the navigation of underwater vehicles. Most previously reported solutions to this alignment calibration problem require the use of absolute navigation fixes of the underwater vehicle, thus requiring additional navigation sensors and/or beacons to be located externally and apart from the underwater vehicle. We briefly review four recently reported alignment calibration methods employing only internal vehicle navigation sensors for velocity, acceleration, attitude, and depth. We report the results of comparative analysis of the performance of these recently reported methods and a previously reported method with navigation data from deep-water survey missions of the Sentry autonomous underwater vehicle conducted in March, 2011 in the Kermadec Arc in the Southern Pacific Ocean. The results reveal consistent differences in performance of the various methods when analyzed on navigation data from several different vehicle dives.

This paper uses rotors in Geometric Algebra to formulate a stable adaptive identifier on the group of rigid body rotations. This identifier is intended to estimate the alignment offsets between independent sensors. The approach provides a straightforward geometric interpretation based on first-order rotor kinematics. Lyapunov theory is used to prove the stability of the identifier, and numerical simulations illustrate its behavior with and without measurement noise.

Autonomous underwater vehicles (AUVs) are commonly used to support oceanographic science by providing water-column mapping, seafloor bathymetric and photographic survey, and deep-sea exploration capabilities. In practice, the mapping activities carried out by AUVs consist of flying either pre-programmed tracklines (most propeller-driven AUVs), or else reporting data to human operators at regular intervals that permit retasking (typical for month-long underwater glider deployments). AUVs equipped with the ability to reason about scientific objectives in real time could significantly increase the value of individual deployments by enabling sampling efforts to be focused on targets or areas identified autonomously or semi-autonomously as scientifically interesting [1]. In this paper, we focus on AUV autonomy as it pertains to water-column sensing and argue that the classification of water-column sensor data represents an important enabling capability. We demonstrate practical, semi-supervised classification of water-column sensor data using a particular Bayesian, non-parametric clustering method, the Variational Dirichlet Process, combined with operator-supplied semantic labeling. The method is applied to the detection of a deep subsea hydrocarbon plume using data collected by the Woods Hole Oceanographic's Sentry AUV during an expedition to the Gulf of Mexico following the Deepwater Horizon blowout disaster.

This paper reports the Sentry autonomous underwater vehicle and its deployment on two cruises in response to the Deepwater Horizon oil spill. The first cruise, in June 2010, coupled Sentry with the TETHYS mass spectrometer to track and localize a subsea hydrocarbon plume at a depth of approximately 1100m going at least 30km from the oil spill site. In December 2010, Sentry mapped and photographed deep-sea biological communities for follow-up observations and sampling with the Alvin manned submersible. These cruises demonstrate how robots and novel sensing technologies contributed to the oil spill assessment and the broader impact of technologies developed for basic research.

This paper reports an overview of the navigation and control system design for the new Nereus hybrid underwater robotic vehicle (HROV). Vehicle performance during its first sea trials in November 2007 near Hawaii, and in May and June 2009 in the Challenger Deep of the Mariana Trench is reported. During the latter expedition, the vehicle successfully performed scientific observation and sampling operations at depths exceeding 10,903 m. The Nereus underwater vehicle is designed to perform scientific survey and sampling to the full depth of the ocean - significantly deeper than the depth capability of all other present-day operational vehicles. For comparison, the second deepest underwater vehicle currently operational worldwide can dive to 7,000 m maximum depth. Nereus operates in two different modes. For broad-area survey, the vehicle can operate untethered as an autonomous underwater vehicle (AUV) capable of exploring and mapping the sea floor with sonars and cameras. Nereus can be converted at sea to become a tethered remotely operated vehicle (ROV) to enable close-up imaging and sampling. The ROV configuration incorporates a lightweight fiber-optic tether (for high-bandwidth, real-time video and data telemetry to the surface), an electro-hydraulic manipulator arm, and sampling instruments. The Nereus vehicle is designed to render all parts of the Earth's seafloor accessible to oceanographic science.

This paper reports the analytical development and preliminary experimental evaluation of a class of exact nonlinear full state model-based observers for underwater vehicle navigation. This class of observers exploits exact knowledge of the vehicle's nonlinear dynamics, the forces and moments acting on the vehicle, and disparate position and velocity measurements. The reported observer is novel in that it estimates the full state of the vehicle and employs Lyapunov techniques to show stability. The performance of the observer is evaluated using data from single degree-of-freedom experiments with a laboratory remotely operated vehicle. High-precision measurements from a 300kHz long baseline (LBL) acoustic positioning systems serve as the basis for evaluating the performance of the observer. Error in the observer position estimate possesses a significantly lower standard deviation than measurements from 12kHz LBL systems alone. The performance of the observer is compared to the extended Kalman filter (EKF) and the error in the position estimates of these two estimators found to be comparable. These experiments are, to the best of our knowledge, the first report of the experimental implementation of exact nonlinear dynamic model-based observers and their comparison to EKFs for underwater vehicle navigation.

This paper reports a novel stable adaptive identifier on the group of rigid-body rotations, and its application to a sensor-calibration problem arising in underwater vehicle navigation. The problem addressed is the identification of an unknown rigid-body rotation map from input-output data. General least-squares (LS) and adaptive identification techniques are commonly employed to identify general linear maps from input-output data, but do not guarantee that the resulting identified map is a rigid-body rotation. At present, an LS singular value decomposition approach is the standard method for identification constrained to the group of rigid-body rotations. This paper reports the first exact adaptive identifier on the group of rigid-body rotations, together with a proof of asymptotic stability. Techniques for navigating underwater vehicles are reviewed, and the Doppler-alignment calibration problem is posed. The reported adaptive identifier is employed to solve this problem, and performance of this adaptive identifier is evaluated on both laboratory and field experimental data. The results reported herein compare favorably with results obtained via previously reported LS techniques. The methodology reported herein may be of broader interest because of its applicability to more general problems in the identification, dynamics, and control on the group of rigid-body motions

This paper reports a novel stable adaptive identifier on the group of rigid body rotations, and its application to a sensor calibration problem arising in underwater vehicle navigation. The problem addressed is the identification of a rigid-body rotation map from input-output data. General least-square and adaptive identification techniques are commonly employed to identify general linear maps from input-output data, but do not guarantee that the resulting identified map is a rigid body rotation. At present, a least-square singular value decomposition approach is the standard method for identification constrained to the group of rigid body rotations. This paper reports the first exact adaptive identifier on the group of rigid body rotations, together with a proof of stability. The performance of this adaptive identifier is evaluated on actual experimental data and found to compare favorably with results obtained via previously reported least-squares techniques. The methodology reported herein is of broader interest because of its applicability to general problems in the identification, dynamics, and control on the group of rigid body motions.

Addresses a practical problem arising in the calibration of bottom-lock Doppler sonar for the navigation of underwater robot vehicles. Employing a least-squares method, the rotational alignment offset between a bottom-lock Doppler sonar and a north-seeking gyroscope can be experimentally determined using sensors commonly deployed with a vehicle in the field. It requires sensor values from the vehicle's Doppler sonar and 3-axis gyroscope, and absolute vehicle position fixes from a long-baseline or short-baseline acoustic navigation system. The performance of the calibration method is evaluated with simulated Doppler data possessing measurement noise typical of that found in actual in-water vehicle sensor data.