Targeted sampling by autonomous underwater vehicles

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zhang, Y., Ryan, J. P., Kieft, B., Hobson, B. W., McEwen, R. S., Godin, M. A., Harvey, J. B., Barone, B., Bellingham, J. G., Birch, J. M., Scholin, C. A., & Chavez, F. P. Targeted sampling by autonomous underwater vehicles. Frontiers in Marine Science, 6 (2019): 415, doi:10.3389/fmars.2019.00415.In the vast ocean, many ecologically important phenomena are temporally episodic, localized in space, and move according to local currents. To effectively study these complex and evolving phenomena, methods that enable autonomous platforms to detect and respond to targeted phenomena are required. Such capabilities allow for directed sensing and water sample acquisition in the most relevant and informative locations, as compared against static grid surveys. To meet this need, we have designed algorithms for autonomous underwater vehicles that detect oceanic features in real time and direct vehicle and sampling behaviors as dictated by research objectives. These methods have successfully been applied in a series of field programs to study a range of phenomena such as harmful algal blooms, coastal upwelling fronts, and microbial processes in open-ocean eddies. In this review we highlight these applications and discuss future directions.This work was supported by the David and Lucile Packard Foundation. The 2015 experiment in Monterey Bay was partially supported by NOAA Ecology and Oceanography of Harmful Algal Blooms (ECOHAB) Grant NA11NOS4780030. The 2018 SCOPE Hawaiian Eddy Experiment was partially supported by the National Science Foundation (OCE-0962032 and OCE-1337601), Simons Foundation Grant #329108, the Gordon and Betty Moore Foundation (Grant #3777, #3794, and #2728), and the Schmidt Ocean Institute for R/V Falkor Cruise FK180310. Publication of this paper was funded by the Schmidt Ocean Institute

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous tracking and sampling of the deep chlorophyll maximum layer in an open-ocean eddy by a long-range autonomous underwater vehicle

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zhang, Y., Kieft, B., Hobson, B. W., Ryan, J. P., Barone, B., Preston, C. M., Roman, B., Raanan, B., Marin,Roman,,III, O'Reilly, T. C., Rueda, C. A., Pargett, D., Yamahara, K. M., Poulos, S., Romano, A., Foreman, G., Ramm, H., Wilson, S. T., DeLong, E. F., Karl, D. M., Birch, J. M., Bellingham, J. G., & Scholin, C. A. Autonomous tracking and sampling of the deep chlorophyll maximum layer in an open-ocean eddy by a long-range autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 45(4), (2020): 1308-1321, doi:10.1109/JOE.2019.2920217.Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor spatial and temporal variation in contextual water column properties. The simultaneous sampling and mapping enabled observation of DCM microbial community in its natural frame of reference.10.13039/501100008982 - National Science Foundation 10.13039/100000936 - Gordon and Betty Moore Foundation 10.13039/100000008 - David and Lucile Packard Foundation 10.13039/100016377 - Schmidt Ocean Institute 10.13039/100000893 - Simons Foundatio

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue

Risk analysis and decision making for autonomous underwater vehicles

Risk analysis for autonomous underwater vehicles (AUVs) is essential to enable AUVs to explore extreme and dynamic environments. This research aims to augment existing risk analysis methods for AUVs, and it proposes a suite of methods to quantify mission risks and to support the implementation of safety-based decision making strategies for AUVs in harsh marine environments. This research firstly provides a systematic review of past progress of risk analysis research for AUV operations. The review answers key questions including fundamental concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future research trends to bridge existing gaps. Based on the state-of-the-art research, a copula-based approach is proposed for predicting the risk of AUV loss in underwater environments. The developed copula Bayesian network (CBN) aims to handle non-linear dependencies among environmental variables and inherent technical failures for AUVs, and therefore achieve accurate risk estimation for vehicle loss given various environmental observations. Furthermore, path planning for AUVs is an effective decision making strategy for mitigating risks and ensuring safer routing. A further study presents an offboard risk-based path planning approach for AUVs, considering a challenging environment with oil spill scenarios incorporated. The proposed global Risk-A* planner combines a Bayesian-based risk model for probabilistic risk reasoning and an A*-based algorithm for path searching. However, global path planning designed for static environments cannot handle the unpredictable situations that may emerge, and real-time replanned solutions are required to account for dynamic environmental observations. Therefore, a hybrid risk-aware decision making strategy is investigated for AUVs to combine static global planning with dynamic local re-planning. A dynamic risk analysis model based on the system theoretic process analysis (STPA) and BN is applied for generating a real-time risk map in target mission areas. The dynamic window algorithm (DWA) serves for local path planning to avoid moving obstacles. The proposed hybrid risk-aware decisionmaking architecture is essential for the real-life implementation of AUVs, leading eventually to a real-time adaptive path planning process onboard the AUV