Award Abstract # 2323050
SCC-PG: Sustainable Vertiports for Bringing Autonomous Drone Swarm Inspection to Oil and Gas Industry Community

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY
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: FY 2023 = $149,999.00
History of Investigator:
  • Sihua Shao (Principal Investigator)
    sihua.shao@mines.edu
  • Xiang Sun (Co-Principal Investigator)
  • Mostafa Hassanalian (Co-Principal Investigator)
  • Khadir El-kaseeh (Co-Principal Investigator)
Recipient Sponsored Research Office: New Mexico Institute of Mining and Technology
801 LEROY PL
SOCORRO
NM  US  87801-4681
(575)835-5496
Sponsor Congressional District: 02
Primary Place of Performance: New Mexico Institute of Mining and Technology
801 LEROY PL
SOCORRO
NM  US  87801-4681
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): HZJ2JZUALWN4
Parent UEI:
NSF Program(s): S&CC: Smart & Connected Commun
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9150, 029E, 042Z
Program Element Code(s): 033Y00
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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(Showing: 1 - 10 of 11)
Cal, Semih and Sun, Xiang and Yao, Jingjing "Client Selection in Fault-Tolerant Federated Reinforcement Learning for IoT Networks" , 2024 https://doi.org/10.1109/ICC51166.2024.10622515 Citation Details
Mannan, Fahad and Hassanalian, Mostafa "Feasibility for a Solar Powered Autonomous Drone Vertiport System" , 2024 https://doi.org/10.2514/6.2024-86042 Citation Details
Mannan, Fahad and Moore, Logan and Shao, Sihua and Sun, Xiang and Hassanalian, Mostafa "Sustainable and Portable Vertiports Enabling Autonomous Drone Swarm Inspection in the Oil and Gas Industry" , 2024 Citation Details
Manu, Daniel and Lin, Youzuo and Yao, Jingjing and Li, Zhirun and Sun, Xiang "Enhancing IoT Security with Asynchronous Federated Learning for Seismic Inversion" , 2024 https://doi.org/10.1109/ICCWorkshops59551.2024.10615411 Citation Details
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Sherman, Michelle and Shao, Sihua and Sun, Xiang and Zheng, Jun "Counter UAV Swarms: Challenges, Considerations, and Future Directions in UAV Warfare" IEEE Wireless Communications , 2024 https://doi.org/10.1109/MWC.003.2400047 Citation Details
Yu, Liangkun and Li, Zhirun and Ansari, Nirwan and Sun, Xiang "Hybrid Transformer Based Multi-Agent Reinforcement Learning for Multiple Unmanned Aerial Vehicle Coordination in Air Corridors" IEEE Transactions on Mobile Computing , 2025 https://doi.org/10.1109/TMC.2025.3532204 Citation Details
Yu, Liangkun and Li, Zhirun and Yao, Jingjing and Sun, Xiang "Transformer-Based Multi-Agent Reinforcement Learning for Multiple Unmanned Aerial Vehicle Coordination in Air Corridors" , 2024 https://doi.org/10.1109/ICCWorkshops59551.2024.10615924 Citation Details
Yu, Liangkun and Sun, Xiang and Albelaihi, Rana and Park, Chaeeun and Shao, Sihua "Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-Synchronous Federated Learning" Electronics , v.13 , 2024 https://doi.org/10.3390/electronics13234585 Citation Details
Yu, Liangkun and Sun, Xiang and Albelaihi, Rana and Yi, Chen "Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning" Future Internet , v.15 , 2023 https://doi.org/10.3390/fi15110352 Citation Details
(Showing: 1 - 10 of 11)

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