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.

Ocean Worlds represent one of the best chances for the discovery of extra-terrestrial life within our own solar system. Liquid oceans are thought to exist on these celestial bodies, often encased in a thick icy shell. In order to investigate these oceans, a new mission concept utilizing a submersible craft must be developed. This vehicle would be required to traverse the icy shell and travel hundreds or even thousands of kilometers to survey the ocean below. In doing this, the vehicle might be out of contact for weeks or months at a time, requiring it to autonomously detect, locate, and study features of interest. Hydrothermal venting is one potential target, due to the unique ecosystems it supports on Earth. We have developed an autonomous, nested search strategy to locate sources of hydrothermal venting based on currently used methods. To test this search technique a simulation environment was developed using a hydrothermal plume dispersion simulation and a vehicle model. We show the effectiveness of the search method in this environment.

The Autonomous Underwater Vehicle (AUV) Sentry is a part of the U.S. National Deep Submergence Facility (NDSF) and is deployed on scientific missions 120 - 200 days per year. Here we present a detailed analysis of failures, failure tracking methods, and failure reduction techniques as used within the Sentry program between 2009 and 2016. We have tabulated data from more than 400 dives conducted on dozens of expeditions with a wide variety of mission objectives that are used to describe a detailed picture of failures, failure rates, and initiatives to address failures. We begin by discussing the nature of Sentry and the missions it is tasked with. We then define metrics for failures and reliability. AUVs are complex systems often comprising both the vehicle and tens of sensors and ancillary systems. Because the vehicle is expected to deliver data for scientific users, we adopt a mission focused view of failure. After defining metrics, we discuss overall reliability rates and the breakdown of when failures occur relative to cruises, overhauls, and major vehicle changes. We present failure pareto charts and discuss the changes in major failure modes as the system has matured.

Recent advances in acoustic navigation methods are enabling extended autonomous underwater vehicles' (AUVs) mission time while maintaining their XY position error within appropriate limits. Furthermore, while advances in inertial sensor technology are drastically lowering the size, power consumption, and cost of these sensors, these sensors remain noisy and accrue error over time. This paper builds on the research and recent developments in single beacon one-way-travel-time acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strapdown sensor suite, relying on a consumer grade microelectromechanical system inertial measurement unit (IMU) and a vehicle's dynamic model velocity. An implementation of an extended Kalman filter that includes IMU bias estimation and coupled with a range filter is obtained in the field on two types of AUVs. Results from these field trials in controlled environments and ocean show that the reported navigation solution possesses an accuracy comparable to existing methods.

The goal of this project was to develop the first algorithms that allow a heterogeneous group of oceanic robots to autonomously determine and implement sampling strategies with the help of numerical ocean forecasts and remotely-sensed observations. Two-way feedback with shore-based numerical models, tested in the field, had not previously been attempted. New planning algorithms were tested during two field programs in Monterey Bay during a 12-month period using three different types of autonomous vehicles.

This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short‐range Iver AUVs, Tethys‐class long‐range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.

The absence of the global positioning system (GPS) is a challenge to navigation and localization in the underwater environment. Thus, for precise and accurate navigation, autonomous underwater vehicles (AUVs) often rely on very precise (yet very expensive and high energy consumption) inertial strap-down sensors which require external observations to constrain their dead-reckoned position error over time — e.g., Doppler velocity logs (DVLs) or acoustic positioning systems. Recent advances in acoustic navigation methodologies are enabling AUVs to extend submerged mission time by providing external position corrections and thereby constraining position error. Additionally, advances in microelectromechanical system (MEMS) inertial sensor technology has drastically lowered the size, power consumption, and cost of inertial sensors; however, they are still noisy and accumulate error over time. This paper builds on recent advances in single beacon one-way-travel-time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying on a consumer grade MEMS inertial measurement unit (IMU) and a vehicle's dynamic model velocity. A real time implementation of an Extended Kalman Filter (EKF), containing bias estimation, is obtained in the field with two Ocean-Server, Inc. Iver2 vehicles, and results show an average position error of 10.26 meters and 12.95 meters over a distance traveled of 1.73 kilometers and 1.91 kilometers, respectively.

This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.

Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean's impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention.

Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

Time-series measurements of diffuse exit-fluid temperature and velocity collected with a new, deep-sea camera, and temperature measurement system, the Diffuse Effluent Measurement System (DEMS), were examined from a fracture network within the ASHES hydrothermal field located in the caldera of Axial Seamount, Juan de Fuca Ridge. The DEMS was installed using the HOV Alvin above a fracture near the Phoenix vent. The system collected 20 s of 20 Hz video imagery and 24 s of 1 Hz temperature measurements each hour between 22 July and 2 August 2014. Fluid velocities were calculated using the Diffuse Fluid Velocimetry (DFV) technique. Over the similar to 12 day deployment, median upwelling rates and mean fluid temperature anomalies ranged from 0.5 to 6 cm/s and 0 degrees C to similar to 6.5 degrees C above ambient, yielding a heat flux of 0.29 +/- 0.22 MW M-2 and heat output of 3.1 +/- 2.5 kW. Using a photo mosaic to measure fracture dimensions, the total diffuse heat output from cracks across ASHES field is estimated to be 2.05 +/- 1.95 MW. Variability in temperatures and velocities are strongest at semidiurnal periods and show significant coherence with tidal height variations. These data indicate that periodic variability near Phoenix vent is modulated both by tidally controlled bottom currents and seafloor pressure, with seafloor pressures being the dominant influence. These results emphasize the importance of local permeability on diffuse hydrothermal venting at mid-ocean ridges and the need to better quantify heat flux associated with young oceanic crust.

Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

Abstract High‐resolution geophysical data have been collected using the Autonomous Underwater Vehicle (AUV) Sentry over the ASHES (Axial Seamount Hydrothermal Emission Study) high‐temperature (~348°C) vent field at Axial Seamount, on the Juan de Fuca Ridge. Multiple surveys were performed on a 3‐D grid at different altitudes above the seafloor, providing an unprecedented view of magnetic data resolution as a function of altitude above the seafloor. Magnetic data derived near the seafloor show that the ASHES field is characterized by a zone of low magnetization, which can be explained by hydrothermal alteration of the host volcanic rocks. Surface manifestations of hydrothermal activity at the ASHES vent field are likely controlled by a combination of local faults and fractures and different lava morphologies near the seafloor. Three‐dimensional inversion of the magnetic data provides evidence of a vertical, pipe‐like upflow zone of the hydrothermal fluids with a vertical extent of ~100 m.

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 [3] [6] 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 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. Previous work in this area includes [4] to generalize the ADCP-aided filter to horizontal motion, including using ADCP beam geometry and a water-volume grid approach for the water velocity state space. Furthermore, a number of extensions are developed in [5] to improve navigation performance during missions characterized by prolonged time-scales. In this paper, the ADCP-aided filter is applied to a 25 hour 5000m deep straight line mission, with the environmental effects considered. Also, the addition of IMU acceleration outputs from a navigation grade IMU for the prediction model as an alternative to the constant velocity (CV) model are implemented and analyzed. The re-acquisition of DVL bottom-lock at the end of the mission, simulating the vehicle lowering altitude to within range of the seafloor, is also investigated.