A future for intelligent autonomous ocean observing systems

Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures

Science-driven Autonomous & Heterogeneous Robotic Networks: A Vision for Future Ocean Observations

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

Optimal sampling paths for autonomous vehicles in uncertain ocean flows

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The focus of this research is to find optimal sampling paths for autonomous vehicles, with the goal of building the most accurate estimate of a velocity field in the shortest time possible. The two main numerical tools employed in this work are the level set method for time-optimal path planning, and the Kalman filter for state estimation and uncertainty quantification. Specifically, the uncertainty associated with the velocity field is defined as the trace of the covariance matrix corresponding to the Kalman filter equations. The novelty in this work is the covariance tracking algorithm, which evolves this covariance matrix along the time-optimal trajectories defined by the level set method, and determines the path expected to minimize the uncertainty corresponding to the flow field by the end of deployment. While finding optimal sampling paths using this method is straightforward for the single-vehicle problem, it becomes increasingly difficult as the number of AVs grows. As such, an iterative procedure is presented here for multi-vehicle problems, which in simple cases can be proven to find controls that collectively minimizes the expected uncertainty, assuming that such a minimum exists. This work demonstrates the utility of combining methods from optimal control theory and estimation theory for learning uncertain fields using fleets of autonomous vehicles. Additionally, it shows that by optimizing over long-duration, continuous trajectories, superior results can be obtained when compared to ad hoc approaches such as a gradient-following control. This is demonstrated for both single-vehicle and multi-vehicle problems, and for static and evolving flow models

Oceanus.

v. 34, no. 1 (1991

Marine Debris Survey Manual

Over the last several years, concern has increased about the amount of man-made materials lost or discarded at sea and the potential impacts to the environment. The scope of the problem depends on the amounts and types of debris. One problem in making a regional comparison of debris is the lack of a standard methodology. The objective of this manual is to discuss designs and methodologies for assessment studies of marine debris. This manual has been written for managers, researchers, and others who are just entering this area of study and who seek guidance in designing marine debris surveys. Active researchers will be able to use this manual along with applicable references herein as a source for design improvement. To this end, the authors have synthesized their work and reviewed survey techniques that have been used in the past for assessing marine debris, such as sighting surveys, beach surveys, and trawl surveys, and have considered new methods (e.g., aerial photography). All techniques have been put into a general survey planning framework to assist in developing different marine debris surveys. (PDF file contains 100 pages.

Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes