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

Perching with fixed wings

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 43-46).Human pilots have the extraordinary ability to remotely maneuver small Unmanned Aerial Vehicles (UAVs) far outside the flight envelope of conventional autopilots. Given the tremendous thrust-to-weight ratio available on these small machines [1, 2], linear control approaches have recently produced impressive demonstrations that come close to matching this agility for a certain class of aerobatic maneuvers where the rotor or propeller forces dominate the dynamics of the aircraft [3, 4, 5]. However, as our flying machines scale down to smaller sizes (e.g. Micro Aerial Vehicles) operating at low Reynold's numbers, viscous forces dominate propeller thrust [6, 7, 8], causing classical control (and design) techniques to fail. These new technologies will require a different approach to control, where the control system will need to reason about the long term and time dependent effects of the unsteady fluid dynamics on the response of the vehicle. Perching is representative of a large class of control problems for aerobatics that requires and agile and robust control system with the capability of planning well into the future. Our experimental paradigm along with the simplicity of the problem structure has allowed us to study the problem at the most fundamental level. This thesis presents methods and results for identifying an aerodynamic model of a small glider at very high angles-of-attack using tools from supervised machine learning and system identification. Our model then serves as a benchmark platform for studying control of perching using an optimal control framework, namely reinforcement learning. Our results indicate that a compact parameterization of the control is sufficient to successfully execute the task in simulation.by Rick E. Cory.S.M

Platform-portable reinforcement learning methods to localize underwater targets

In this study, we present a platform-portable deep reinforcement learning method that has been used as a path-planning system to localize underwater objects with autonomous vehicles.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work also received financial support from the Spanish Ministerio de Economía y Competitividad (BITERECO: PID2020-114732RBC31). This work acknowledges the ’Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).Peer ReviewedPostprint (author's final draft

An End-to-End Platform for Autonomous Dynamic Soaring in Wind Shear

Despite advancements in our understanding of flight in modern times, birds remain unmatched when it comes to maneuverability and energy efficiency in flight; in particular seabirds like the albatross are known to travel vast distances without stopping for food by performing an aerobatic maneuver called dynamic soaring. When the maneuver is executed in the presence of a wind field that varies in strength of direction, the albatross extracts kinetic energy from the field. In this dissertation, we present an end-to-end system designed to exploit wind as the albatross does. The system we designed consists of a gliding platform outfitted with sensors and computational hardware, an on-board software platform that enables autonomy, and a ground platform for monitoring mission performance and issuing commands.We contribute the design of an airframe, the Fox, capable of performing dynamic soaring at low altitudes (~400m above sea level). We validate the airframe against expected stressors (vibration, coefficient of lift, temperature, and communication signal strength), and show in simulation it can complete a dynamic soaring orbit in wind shear that varies in maximum wind speed from 8 to 12 m/s. We show that this airframe can reach speeds exceeding 40 m/s while soaring.We fit the airframe with a commercial off-the-shelf autopilot, as well as a custom on-board-computing (OBC) solution to provide the necessary facilities to enable autonomy. The OBC generates dynamic soaring trajectories that fit a wind-field map that is built as the aircraft is deployed and controls the Fox to follow them by sending commands to the autopilot using a sample-based controller scheme. This process is monitored by human operators on the ground via a portable ground station that is linked to the Fox via a radio antenna. Field tests are presented that validate real-world controller performance against simulated results.Finally, we present a learning controller that learns from and out-performs the sample-based controller in simulation. While not field tested, we believe a self-optimizing controller of this form is necessary to enable autonomy of a soaring aircraft subject to extended mission durations.While dynamic soaring field tests were not pursued in this work, we hope this dissertation will be a blueprint for future researchers to finally achieve autonomous soaring

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Design of a Mobile Underwater Charging System

Autonomous Underwater Vehicles (AUVs) are extremely capable vehicles for numerous ocean related missions. AUVs are energy limited, resulting in short mission endurance on the scale of hours to days. Underwater Gliders (UGs) are able to operate on the order of months to years by using nontraditional propulsion methods. UGs, however, are unable to perform missions requiring high speed or direct forward motion due to the nature of their buoyancy driven motion. This work reviews the current state of the art in recharging AUVs and offers an underwater recharging network concept at a significantly reduced cost to traditional methods. The solution includes the design of a UG capable of serving as charge carrying agent that couples with and charges AUVs autonomously. The vehicle design is built on the work done previously at the Nonlinear and Autonomous Systems Lab on the development of ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering). The ROUGHIE2 design is a rethinking of the original ROUGHIE capabilities to serve as a mobile charger by increasing depth rating, endurance, and payload capacity. The recharging concept presented will be easy to adapt to many different AUVs and UGs making this technology universal to small AUVs

Armstrong Flight Research Center Research Technology and Engineering 2017

I am delighted to present this report of accomplishments at NASA's Armstrong Flight Research Center. Our dedicated innovators possess a wealth of performance, safety, and technical capabilities spanning a wide variety of research areas involving aircraft, electronic sensors, instrumentation, environmental and earth science, celestial observations, and much more. They not only perform tasks necessary to safely and successfully accomplish Armstrong's flight research and test missions but also support NASA missions across the entire Agency. Armstrong's project teams have successfully accomplished many of the nation's most complex flight research projects by crafting creative solutions that advance emerging technologies from concept development and experimental formulation to final testing. We are developing and refining technologies for ultra-efficient aircraft, electric propulsion vehicles, a low boom flight demonstrator, air launch systems, and experimental x-planes, to name a few. Additionally, with our unique location and airborne research laboratories, we are testing and validating new research concepts. Summaries of each project highlighting key results and benefits of the effort are provided in the following pages. Technology areas for the projects include electric propulsion, vehicle efficiency, supersonics, space and hypersonics, autonomous systems, flight and ground experimental test technologies, and much more. Additional technical information is available in the appendix, as well as contact information for the Principal Investigator of each project. I am proud of the work we do here at Armstrong and am pleased to share these details with you. We welcome opportunities for partnership and collaboration, so please contact us to learn more about these cutting-edge innovations and how they might align with your needs