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Autonomous Environmental Monitoring and Management Platform for Remote Habitats

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0316
Agency Tracking Number: 205080
Amount: $122,955.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T4
Solicitation Number: STTR_20_P1
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-23
Award End Date (Contract End Date): 2021-09-30
Small Business Information
8430 Central Ave
Newark, CA 94560-3457
United States
DUNS: 077166385
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 David Peaslee
 (510) 574-8300
 dpeaslee@spec-sensors.com
Business Contact
 Edward Stetter
Phone: (510) 574-8300
Email: efs@spec-sensors.com
Research Institution
 Curators of the University of Missouri on Behalf of UMSL
 
One University Boulevard
St. Louis, MO 63121-4400
United States

 Nonprofit college or university
Abstract

An automated mobile air quality (AQ) sensor array will provide high quality environmental data within the confined physical parameters of a space habitat. Project results, including generated data, will be used to develop algorithms for artificial intelligence (AI) which will ultimately automate monitoring of experiments as well as life support systems on the International Space Station (ISS), the Lunar Gateway, and beyond.Initially, SPEC Sensors will demonstrate a platform for AQ monitoring in a form factor compatible with autonomous robots such as the Astrobee, currently in use aboard the ISS National Laboratory. In this phase, ground-based laboratory evaluations will be performed with a mobile prototype to address flight certification requirements necessary for integration with the current Astrobee fleet, and for safe delivery to the ISS National Lab by Magnitude.io. These experiments will also generate the training data for the proposed machine learning algorithms developed by the University of Missouri ndash; St. Louis. The complexity of these algorithms will determine hardware requirements for the final phases of the project. For example, a reference array mounted on a mobile robot, in conjunction with remote fixed arrays will require a unique implementation of edge-computing methods and mesh-compatible hardware.In Phase II, the platform will perform passive AQ monitoring from the payload bay of an Astrobee during sorties on the ISS. The temporal, spatial, and physical environmental data collected from these flights will generate real training data for machine learning development, and require minimal support from the stationrsquo;s crew. Finally, in Phase III of this project, with updated software and hardware, this system will be provided to NASA and the ISS National Lab for integration into current and future operational systems. With the successful demonstration of this technology, we expect other needs will arise that can be solved with this AI enabled array.

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

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