Dynamic Underwater Glider Network for Environmental Field Estimation

A coordinated dynamic sensor network of autonomous underwater gliders to estimate three-dimensional time-varying environmental fields is proposed and tested. Integration with a network of surface relay nodes and asynchronous consensus are used to distribute local information and achieve the global field estimate. Field spatial sparsity is considered, and field samples are acquired by compressive sensing devices. Tests on simulated and real data demonstrate the feasibility of the approach with relative error performance within 10

Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

The aim of this work is to report on an activity carried out during the 2010 Recognized Environmental Picture experiment, held in the Ligurian Sea during summer 2010. The activity was the first at-sea test of the recently developed decision support system (DSS) for operation planning, which had previously been tested in an artificial experiment. The DSS assesses the impact of both environmental conditions (meteorological and oceanographic) and non-environmental conditions (such as traffic density maps) on people and assets involved in the operation and helps in deciding a course of action that allows safer operation. More precisely, the environmental variables (such as wind speed, current speed and significant wave height) taken as input by the DSS are the ones forecasted by a super-ensemble model, which fuses the forecasts provided by multiple forecasting centres. The uncertainties associated with the DSS's inputs (generally due to disagreement between forecasts) are propagated through the DSS's output by using the unscented transform. In this way, the system is not only able to provide a traffic light map (run/not run the operation), but also to specify the confidence level associated with each action. This feature was tested on a particular type of operation with underwater gliders: the glider surfacing for data transmission. It is also shown how the availability of a glider path prediction tool provides surfacing options along the predicted path. The applicability to different operations is demonstrated by applying the same system to support diver operations

Deployment of a persistent underwater acoustic sensor network: The CommsNet17 Experience

This paper presents the experimental activities performed by the NATO STO Centre for Maritime Research and Experimentation (CMRE) during the CommsNet17 trial where a persistent Underwater Acoustic Sensor Network (UASN) was deployed. The CommsNet17 trial was held from the 27th of November to the 6th of December in the Gulf of La Spezia (IT), close to the CMRE premises, using the CMRE Littoral Ocean Observatory Network (LOON) as one of its key components. A network consisting of up to eleven nodes was deployed, including static and mobile assets. Various aspects related to persistent UASNs were addressed, including autonomous and distributed network discovery and node configuration, node localisation and navigation, self-adjustment of the network topology in support to the assigned tasks, underwater docking, wireless battery recharging and data offloading. The collected results show that the employed solutions were able to successfully complete all these tasks, thus demonstrating the effective deployment of a persistent, distributed and ad-hoc UASN

Satellites to the Seafloor: Autonomous Science to Forge a Breakthrough in Quantifying the Global Ocean Carbon Budget

Understanding the global carbon budget and its changes is crucial to current and future life on Earth. The marine component represents the largest reservoir of the global carbon cycle. In addition to physical processes that govern carbon fluxes at the air-sea interface and regulate the atmospheric carbon budget, complex internal sources and sinks, including inorganic, geologic, microbiological and biological processes also impact carbon distributions and storage. Therefore, it is essential to observe and understand the whole system. This is a daunting task, as many of the processes are distributed throughout the ocean, laterally and vertically over scales ranging from centimeters to thousands of kilometers. Ship and satellite observations both offer a partial view but, for ships, are either too short term and localized and satellites, despite their large spatial coverage, lack the spatial resolution. Ocean robots, such as deep diving autonomous underwater vehicles (AUVs) and gliders, provide in-situ observations of the seafloor and water column while the surface can be observed in-situ by autonomous surface vehicles (ASVs). Presently, these assets are used disparately with each operating independently and requiring direct human intervention for data interpretation and mission retasking. This paradigm is insufficient for the task of obtaining the millions of in-situ and remote measurements necessary for quantifying the ocean’s contribution to the global carbon cycle. This study brings together scientists, who understand the imperative and scope of quantifying the global carbon budget, with technologists, who may be able to glimpse a possible way of solving it. A coordinated network of ocean robots and satellites that autonomously interpret data and communicate sampling strategies could significantly advance our ability to monitor the marine carbon (and other biogeochemical) cycles. The principal goal of this study is to determine whether emerging technologies could enable crucial oceanographic and space science investigations to be coordinated to address this scientific challenge and may be the way to address others. Specifically, we will: establish a lingua franca between the participants’ different research communities that will enable increased communication; identify the observational capabilities required to quantify the carbon cycle; assess the present capabilities of the ocean robotics, autonomous science, and satellite communities to provide these capabilities; investigate if coordinated ocean robots and satellites using autonomous science can obtain those observations; and develop a collaborative research agenda aimed at solving these problems

A Virtual Ocean framework for environmentally adaptive, embedded acoustic navigation on autonomous underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2021.Autonomous underwater vehicles (AUVs) are an increasingly capable robotic platform, with embedded acoustic sensing to facilitate navigation, communication, and collaboration. The global positioning system (GPS), ubiquitous for air- and terrestrial-based drones, cannot position a submerged AUV. Current methods for acoustic underwater navigation employ a deterministic sound speed to convert recorded travel time into range. In acoustically complex propagation environments, however, accurate navigation is predicated on how the sound speed structure affects propagation. The Arctic’s Beaufort Gyre provides an excellent case study for this relationship via the Beaufort Lens, a recently observed influx of warm Pacific water that forms a widespread yet variable sound speed lens throughout the gyre. At short ranges, the lens intensifies multipath propagation and creates a dramatic shadow zone, deteriorating acoustic communication and navigation performance. The Arctic also poses the additional operational challenge of an ice-covered, GPSdenied environment. This dissertation demonstrates a framework for a physics-based, model-aided, real-time conversion of recorded travel time into range—the first of its kind—which was essential to the successful AUV deployment and recovery in the Beaufort Sea, in March 2020. There are three nominal steps. First, we investigate the spatio-temporal variability of the Beaufort Lens. Second, we design a human-in-the-loop graphical decision-making framework to encode desired sound speed profile information into a lightweight, digital acoustic message for onboard navigation and communication. Lastly, we embed a stochastic, ray-based prediction of the group velocity as a function of extrapolated source and receiver locations. This framework is further validated by transmissions among GPS-aided modem buoys and improved upon to rival GPS accuracy and surpass GPS precision. The Arctic is one of the most sensitive regions to climate change, and as warmer surface temperatures and shrinking sea ice extent continue to deviate from historical conditions, the region will become more accessible and navigable. Underwater robotic platforms to monitor these environmental changes, along with the inevitable rise in human traffic related to trade, fishing, tourism, and military activity, are paramount to coupling national security with international climate security.Office of Naval Research (N00014-14-1-0214) — GOATS’14 Adaptive and Collaborative Exploitation of 3-Dimensional Environmental Acoustics in Distributed Undersea Networks Draper Laboratory Incorporated (SC001-0000001039) — Positioning System for Deep Ocean Navigation (POSYDON) Office of Naval Research (N00014-16-1-2129) — MURI: The Information Content of Ocean Noise: Theory and Experiment Office of Naval Research (N00014-17-1-2474) — Environmentally Adaptive Acoustic Communication and Navigation in the New Arctic Office of Naval Research (N00014-19-1-2716) — TFO: Assessing Realism and Uncertainties in Navy Decision Aids Department of Defense, Office of Naval Research — National Defense, Science, and Engineering Graduate Fellowshi

Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 2019 Annual Report

Prepared for: Dr. Brian Bingham, CRUSER DirectorThe Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) provides a collaborative environment and community of interest for the advancement of unmanned systems (UxS) education and research endeavors across the Navy (USN), Marine Corps (USMC) and Department of Defense (DoD). CRUSER is a Secretary of the Navy (SECNAV) initiative to build an inclusive community of interest on the application of unmanned systems (UxS) in military and naval operations. This 2019 annual report summarizes CRUSER activities in its eighth year of operations and highlights future plans.Deputy Undersecretary of the Navy PPOIOffice of Naval Research (ONR)Approved for public release; distribution is unlimited

Environmental Monitoring using Autonomous Vehicles: A Survey of Recent Searching Techniques

Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles