Highly Integrateable AI Modules for Planning, Scheduling, Characterization, and Diagnosis

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0028
Agency Tracking Number: 181093
Amount: $749,989.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: T3
Solicitation Number: STTR_18_P2
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-12-19
Award End Date (Contract End Date): 2021-12-18
Small Business Information
1650 South Amphlett Boulevard, Suite 300, San Mateo, CA, 94402-2516
DUNS: 608176715
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Richard Stottler
 (650) 931-2714
 stottler@stottlerhenke.com
Business Contact
 Nate Henke
Phone: (650) 931-2719
Email: nhenke@stottlerhenke.com
Research Institution
 Montana State University
 Office of Sponsored Programs, PO Box 172470
Bozeman, MT, 59717-2470
 Federally funded R&D center (FFRDC)
Abstract
We will extend our previous work to create artificial intelligence (AI) Reasoning Modules for planning, scheduling, characterization, machine learning, and fault detection/diagnosis/reconfiguration for spacecraft and their subsystems, each able to operate in standalone fashion or be easily integrated with one another to execute in a variety of computational environments, including in highly distributed situations.nbsp; We will integrate our existing AI Modules within NASArsquo;s core Flight System (cFS) so that they can be used (through cFS) on a wide variety of spacecraft, from large manned vehicles to small scientific instruments. We will also integrate the AI Modules on MSUrsquo;s RadPC (radiation tolerant processing CPUs) in an experiment onboard the ISS.nbsp; In addition to an inflight demonstration of our AI modules, this will greatly accelerate the maturation of MSUrsquo;s RadPC, which replaces $200,000 RAD750 radiation hardened processing with equivalent processing power in $100 FPGA chips using soft-CPUs, quadruple redundancy, and FPGA reconfiguration for seamless recovery, achieving 3 orders of magnitude reduction in cost as well as significantly reduced CPU electrical power.nbsp; The ISS experiment will fly for six months and feature two RadPC boards, one of which will be utilizing the full suite of AI Modules to monitor, detect, diagnose, and recover the other RadPC board as well as its own, providing an inflight demonstration for both RadPC and for the AI Modules.nbsp;nbsp;The modules will utilize cFSrsquo;s Software messaging Bus (SB) and the networking version (SBN) to provide the integration mechanism for either local or distributed applications.nbsp; A specific spacecraft mission could utilize the AI Scheduler merely by sending it tasks, resources, and constraints in the defined messaging format across the SB or SBN.nbsp; A different application could use a different AI Module; Characterization, for example.nbsp; A third might use all of the AI Reasoning applications.

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

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