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Demonstration of Space-Qualified Environmental Evaluation Drones with Wireless Intelligent Networked Data Processing (SPEEDWINDs)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC22PB105
Agency Tracking Number: 221898
Amount: $156,446.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T10
Solicitation Number: STTR_22_P1
Timeline
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-07-25
Award End Date (Contract End Date): 2023-08-25
Small Business Information
6201 East Oltorf Street, Suite 400
Austin, TX 78741-7509
United States
DUNS: 100651798
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Sarik
 (832) 293-3906
 jsarik@nanohmics.com
Business Contact
 Lea Lundin
Phone: (512) 389-9990
Email: llundin@nanohmics.com
Research Institution
 Duke University
 
103 Allen Building
Durham, NC 27708-3399
United States

 Federally Funded R&D Center (FFRDC)
Abstract

Deploying autonomous environmental monitoring hardware on Gateway is challenging because of the harsh radiation environment. Existing unmanned aerial vehicles (UAVs) used for environmental monitoring on the International Space Station (ISS) use commercial-off-the-shelf (COTS) components. To enable continuous unsupervised environmental monitoring at Gateway, Nanohmics Inc., in collaboration with Dr. Maria Gorlatova at Duke University, proposes to demonstrate SPace-Qualified Environmental Evaluation Drones with Wireless Intelligent Networked Data Processing (SPEEDWINDs). Each SPEEDWIND will have four key components: 1) a core control system built with inherently radiation hardened components, 2) a high performance COTSnbsp;embedded system to enable machine learning, 3) an environmental monitoring payload with customized mission specific sensors, and 4) a wireless transceiver with adaptive networking to enable distributed operation. In Phase I, Nanohmics proposes to design a benchtop SPEEDWIND testbed combines space qualified and COTS components and demonstrate the ability of the testbed to perform distributed machine learning, such as processing fluorescence spectroscopy data, in a simulated Gateway environment.

* Information listed above is at the time of submission. *

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