NASA SBIR 2019-II Solicitation

Proposal Summary

 19-2- H6.22-3264
 Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
 Neuromorphic Spacecraft Fault Monitor
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Exploration Institute, LLC
710 North Post Oak Road #400
Houston, TX 77024
(617) 599-0774

PRINCIPAL INVESTIGATOR (Name, E-mail, Mail Address, City/State/Zip, Phone)
Michael Mercury
710 North Post Oak Road #400
Houston, TX 77024
(626) 269-8248

BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Armin Ellis
710 North Post Oak Road #400
Houston, TX 77024 - 1111
(617) 599-0774

Estimated Technology Readiness Level (TRL) :
Begin: 6
End: 7
Technical Abstract (Limit 2000 characters, approximately 200 words)

The goal of this work is to develop a low power machine learning anomaly detector. The low power comes from the type of machine learning (Spiking Neural Network (SNN)) and the hardware the neuromorphic anomaly detector runs on. The ability to detect and react to anomalies in sensor readings on board resource constrained spacecraft is essential, now more than ever, as enormous satellite constellations are launched and humans push out again beyond low Earth orbit to the Moon and beyond.

Spacecraft are autonomous systems operating in dynamic environments. When monitored parameters exceed limits or watchdog timers are not reset, spacecraft can automatically enter a 'safe' mode where primary functionality is reduced or stopped completely. During safe mode the primary mission is put on hold while teams on the ground examine dozens to hundreds of parameters and compare them to archived historical data and the spacecraft design to determine the root cause and what corrective action to take. This is a difficult and time consuming task for humans, but can be accomplished faster, in real-time, by machine learning.

As humans travel away from Earth, light travel time delays increase, lengthening the time it takes for ground crews to respond to a safe mode event. The few astronauts onboard will have a hard time replacing the brain power and experience of a team of experts on the ground. Therefore, a new approach is needed that augments existing capabilities to help the astronauts in key decision moments.

We provide a new machine learning approach that recognizes nominal and faulty behavior, by learning during integration, test, and on-orbit checkout. This knowledge is stored and used for anomaly detection in a low power neuromorphic chip and continuously updated through regular operations. Anomalies are detected and context is provided in real-time, enabling both astronauts onboard, and ground crews on Earth, to take action and avoid potential faults or safe mode events.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

The software developed in Phase II can potentially be used by NASA for anomaly detection onboard the ISS, the planned Lunar Gateway, and future missions to Mars. The NSFM software can also be used by ground crews to augment their ability to monitor spacecraft and astronaut health telemetry once it reaches the ground. The NSFM software can furthermore be used during integration and test to better inform test operators of the functionality of the system during tests in real time.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

The software developed in Phase II can potentially be used for anomaly detection onboard any of the new large constellations planned by private companies. It can also be applied to crewed space missions, deep space probes, UUVs, UAVs, and many industrial applications on Earth. The NSFM software developed in Phase II can also be used during Integration and Test of any commercial satellite. 

Duration: 24

Form Generated on 05/04/2020 06:27:37