The development of ocean test beds for ocean technology adaptation and integration into the emerging U.S. offshore wind energy industry

The landscape of applied ocean technology is rapidly changing with forces of innovation emerging from basic ocean science research methodologies as well as onshore high tech sectors. There is a critical need for ocean-related industries to continue to modernize via the adoption of state-of-the-art practices to advance rapidly changing industry objectives, maintain competitiveness, and be careful stewards of the ocean as a common resource. These objectives are of national importance for the dynamic ocean energy sector, and a mechanism by which new and promising technologies can be validated and adopted in an open and benchmarked process is needed. POWER-US seeks to develop Ocean Test Beds as research and development infrastructure capable of driving innovative observations, modeling, and monitoring of the physical, biological, and use characteristics present in offshore wind energy installation areas.AK acknowledges internal support from the Woods Hole Oceanographic Institution via the Houghton Foundation Award

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics and Astronautics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which requires a mobile vehicle to efficiently find and densely sample from the most scientifically valuable region in an a priori unknown, dynamic environment. The PLUMES algorithm — Plume Localization under Uncertainty using Maximum-ValuE information and Search—is subsequently presented, which addresses the MSS problem and overcomes key technical challenges with planning in natural environments. Theoretical performance guarantees are derived for PLUMES, and empirical performance is demonstrated against canonical uniform search and state-of-the-art baselines in simulation and field trials. Ultimately, this thesis examines the challenges of autonomous informative sampling in the environmental and earth sciences. In order to create useful systems that perform diverse scientific objectives in natural environments, approaches from robotics planning, field design, Bayesian optimization, machine learning, and the sciences must be drawn together. PLUMES captures the breadth and depth required to solve a specific objective within adaptive sampling, and this work as a whole highlights the potential for mobile technologies to perform intelligent autonomous science in the future

Physics and Ecology in the Marginal Ice Zone of the Fram Strait : a Robotic Approach

This thesis describes operations of an autonomous underwater vehicle (AUV) to investigate the complex interaction between physical forcing and ecological response in the marginal ice zone of the Fram Strait. The vehicle was equipped with instruments collecting physical, chemical, and biological data in the euphotic zone (0 - 50 m depth). After an introductory part, the thesis consists of six studies. The first four studies have a technical focus and they describe the integration of a water sample collector, sensors and a payload control computer. Additionally, supporting technologies such as flying drones and a filter to correct the AUV s navigation data are described. The fifth study tackles the issue of the purity and safety of the water samples inside the AUV. The last study has a scientific focus and presents the first direct observations of wind driven frontogenesis along a melt water front. Vehicle data were complemented by means of ship and model based data to explain the observed hydrographic structures and the distribution of chlorophyll a. In the final section of this thesis, open scientific questions and possible technological upgrades are presented