
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | July 18, 2023 |
Latest Amendment Date: | July 18, 2023 |
Award Number: | 2323050 |
Award Instrument: | Standard Grant |
Program Manager: |
Oleg Sokolsky
osokolsk@nsf.gov (703)292-4760 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2023 |
End Date: | September 30, 2025 (Estimated) |
Total Intended Award Amount: | $149,999.00 |
Total Awarded Amount to Date: | $149,999.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
801 LEROY PL SOCORRO NM US 87801-4681 (575)835-5496 |
Sponsor Congressional District: |
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Primary Place of Performance: |
801 LEROY PL SOCORRO NM US 87801-4681 |
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): | S&CC: Smart & Connected Commun |
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): | 47.070 |
ABSTRACT
This NSF Smart and Connected Community (S&CC) planning grant will set the groundwork for exploring a sustainable vertiport system capable of deploying autonomous drone swarms for methane emission measurements over orphaned wells. The planning grant will also scrutinize the responses of regulators and operators to the potential technological changes. These abandoned oil or gas wells, typically left behind by the fossil fuel extraction industry when operating expenses outstrip production rates, contribute significantly to greenhouse gas emissions. Given the high costs associated with the plugging, remediation, and restoration of these wells, robust, data-driven evidence is required to justify and prioritize the allocation of state and federal funds. Traditional methane emission measurements, involving flux chamber installation at each open wellhead, carry high capital and operational costs and are challenging to deploy in hard-to-reach areas. While primarily targeting the oil and gas industry community, the research outcomes could offer valuable insights applicable to diverse areas such as wildlife monitoring, anti-poaching initiatives, infrastructure and aircraft inspections, construction site surveillance, and water pollution monitoring.
The research aims to create a new cross-domain framework for an integrated, sustainable vertiport that aids an autonomous drone swarm inspection system. The project revolves around three technical objectives: 1) Developing a low-cost, portable, and sustainable vertiport to facilitate precision landing and housing, protection, and recharging of multiple drones; 2) Constructing a safe federated deep reinforcement learning algorithm to enable drone swarm landing and takeoff in harsh environments; 3) Examining three-dimensional drone swarm path planning for efficient methane plume localization and emission quantification. Simultaneously, the project will pursue two social science objectives: 1) Quantitative assessment of potential efficiency and equity improvements in federal funds allocated for cleaning up orphaned wells; 2) Encouraging operators to adopt this cost-effective monitoring system by enhancing equity in carbon dioxide sequestration tax incentives. The research outcomes will revolutionize measurement and monitoring technologies, enabling the oil and gas industry to identify economically viable and sustainable solutions to reduce greenhouse gas emissions, while providing valuable insights and tools applicable to high-impact areas such as airborne wireless edge computing and autonomous drone swarm defense.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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