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Environmental Monitoring Microsensor Array (EMMA) for Free Flying Robots

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0361
Agency Tracking Number: 204921
Amount: $124,991.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: Z5
Solicitation Number: SBIR_20_P1
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-25
Award End Date (Contract End Date): 2021-03-01
Small Business Information
1585 Marauder Street
Chico, CA 95973-9064
United States
DUNS: 933302655
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Darby Makel
 (530) 895-2771
 dmakel@makelengineering.com
Business Contact
 Darby Makel
Phone: (530) 895-2771
Email: dmakel@makelengineering.com
Research Institution
N/A
Abstract

Makel Engineering, Inc. proposes to develop a highly compact Environmental Microsensor Array (EMMA) as a payload for free flying Intra-Vehicular Activity (IVA) robots, such as NASArsquo;s Astrobee. EMMA will include machine learning to translate and interpret onboard sensor data (e.g., chemicals, temperature, humidity, pressure, acoustic, etc.) within the context of planetary facilities. Planned human exploration beyond Earth orbit will rely on an orbiting facility near the Moon, called lsquo;Gatewayrsquo; with intermittent human occupation, requiring robust autonomous inspection and diagnostics tools. EMMArsquo;s sensors and machine learning algorithms will establish nominal background conditions throughout the vehicle, to easily identify anomalies and trigger further action. For instance, fast leak of pressurized lines may be detected quickly via change in noise levels, which would trigger EMMA to look for small changes in pressure level and small changes in chemical signatures, which combined and compared to nominal background conditions using machine learning tools, provide early diagnostics data that would enable quick mitigation actions (e.g., isolation by valves, selectively shutting down systems, etc.).nbsp;Phase I will identify Gateway use cases to define EMMArsquo;s requirements and scenarios for occupied and vacant periods. EMMA onboard COTS autonomous robots (e.g., floor cleaning robots) will navigate staged occupied or vacant rooms, simulating the nominal and fault conditions of real scenarios. Phase I machine learning algorithms will be deployed on external computers.nbsp;Phase II will mature the technology taking from lab demonstration to a series of flyable prototypes which will be delivered to NASA for testing in ongoing microgravity experiments and integration with Astrobee at NASA ground-based testbeds at ARC and JSC during the program.. The machine learning algorithms will be migrated to the embedded processors, resulting in a standalone prototype system.

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

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