| NSF Org: |
OPP Office of Polar Programs (OPP) |
| Recipient: |
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| Initial Amendment Date: | August 13, 2019 |
| Latest Amendment Date: | August 13, 2019 |
| Award Number: | 1945924 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Olivia Lee
OPP Office of Polar Programs (OPP) GEO Directorate for Geosciences |
| Start Date: | September 1, 2020 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $293,134.00 |
| Total Awarded Amount to Date: | $293,134.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
75 LOWER COLLEGE RD RM 103 KINGSTON RI US 02881-1974 (401)874-2635 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
215 South Ferry Road Narragansett RI US 02882-1197 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | AON-Arctic Observing Network |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.078 |
ABSTRACT
In the Arctic, air-sea interactions and biological-chemical processes in the ocean are strongly affected by the extent and thickness of sea ice cover. Presently, due to safety concerns, data collection involving Unmanned Underwater Vehicles (UUVs) in the Arctic typically operate far away from the ice-water boundary and are recovered using customized systems. As a consequence, critical scientific processes (e.g., phytoplankton blooms and air-sea exchanges at offshore leads in the sea ice) are under-sampled. Challenges still exist for navigating UUVs in the ice-covered ocean, especially in proximity to the ice shelf. The goal of this project is to develop and test a safe, long-distance autonomous UUV-based instrument for under-ice observations and data collection by designing and implementing a new advanced system for near ice-water interface measurements through the use of multiple sensors and in situ decision-making including artificial intelligence algorithms.
This project aims to develop and test an enhanced Unmanned Underwater Vehicle (UUV) system with an accurate ice-relative localization solution, an in-situ collision avoidance re-planning mechanism, and a robust water-opening detection capability. Specifically, the primary objective is to develop new underwater autonomous sampling capabilities that will provide critical measurements and observations for advancing our knowledge about under-ice biological productivity and the physical-chemical transports at the air-ice-water boundary. To make it adaptable to a variety of UUVs, a suite of low size, weight, power, and cost (SWAP-C) sensors will be selected, and the algorithms will be developed using open-source software. The system will localize the vehicle relative to the ice via fusing the inertial measurements and the perception information (e.g., ice topography, texture, and air bubbles). During under-ice operations, the system will also detect ice keels and extrusions, then adapt its path for collision avoidance if necessary. Finally, the designed navigation system will perform a robust detection of water openings in the sea ice. This will allow a safe UUV surfacing event for transmitting data, updating mission plans, and collecting unique cross-boundary measurements at the ice openings. In this project, the developed navigation system will be integrated on a portable underwater robot and tested in a frozen freshwater pond and a subpolar lake. A compact science sensor suite will also be attached to search for under-ice blooms at the ice-water interface. This project also supports an early career scientist and includes a commitment to education and training by involving undergraduate students in data processing and analysis, as well as outreach and science communication activities that engage a broader audience using varied community and media outlets.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Ice-water horizon is a critical but under-sampled region for studying various biogeochemical processes (e.g., phytoplankton blooms and air-sea exchanges at offshore leads) in polar regions. This project seeks to develop new algorithms to overcome the challenges of navigating Unmanned Underwater Vehicles (UUVs) at the ice-water interface. Specifically, our project has three focuses: localizing the vehicle using a suite of small size, weight, power, and low-cost sensors with a low drift rate, realizing online replanning for avoiding ice keels and extrusions, and detecting water openings for possible vehicle surfacing events to offload data and update missions. During this project, two major field trials were conducted. The first field trial was conducted in Michigan, the ROV was deployed multiple times under 30cm thick freshwater ice, while the second trial was conducted in Utqiagvik, Alaska, where the sea-ice is about 1.5 meters thick. These field trials have approved the robustness of the developed portable ROV system and provided comprehensive datasets for developing under-ice localization algorithms.
On the technical side, the research team has created a new multi-sensor fusion framework for underwater vehicles to fuse motion sensors with perceptual sensors (such as cameras and imaging sonars). We have found reduced localization errors when the camera images are integrated for estimating the underwater vehicle?s position. Overall, the framework produced a drift rate of about 2-3% based on the field datasets. Sensor-based online path planning is also critical for UUVs to avoid collision with ice keels and extrusion. To this end, a sampling-based online coverage path planner (SO-CPP) was developed, which guides the UUVs to cover a user-defined area at a high ratio (over 99.5%) while avoiding collisions with priorly unknown obstacles. We have validated the robustness of the algorithm through repeated simulations in different scenarios, e.g., obstacle-free, obstacle-occupied, and obstacle-crowded environments. In addition to the localization and guidance algorithm, a water-opening pipeline is created based on the existing imaging processing methods. The algorithm could be integrated into UUV operation to allow safe surfacing through leads and water openings. This feature is especially beneficial for long-distance UUV under-ice surveys where we could offload data and update the mission during the operation.
Last Modified: 12/28/2022
Modified by: Mingxi Zhou
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