NASA SBIR 2020-I Solicitation

Proposal Summary


PROPOSAL NUMBER:
 20-1- H9.03-4830
SUBTOPIC TITLE:
 Flight Dynamics and Navigation Technology
PROPOSAL TITLE:
 Neural Space Navigator
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Princeton Satellite Systems
6 Market Street, Suite 926
Plainsboro, NJ 08536
(609) 279-9606

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Michael Paluszek
E-mail:
map@psatellite.com
Address:
6 Market Street, Suite 926 Plainsboro, NJ 08536 - 2096
Phone:
(609) 275-9606

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Michael Paluszek
E-mail:
map@psatellite.com
Address:
6 Market Street, Suite 926 Plainsboro, NJ 08536 - 2096
Phone:
(609) 275-9606
Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 4
Technical Abstract (Limit 2000 characters, approximately 200 words)

The Neural Space Navigator (NSN) is an innovative fully autonomous navigation system for deep space or near the surface of moons, asteroids or planets, It employs dual cameras. One is on a pan and tilt platform for tracking celestial objects and for terrain tracking. The other is a star camera that provides a star reference. An optional sun distance sensor provides a sun range  reference in interplanetary space. The terrain tracker is a neural network trained using Deep Learning. Training is done offline from images taken by other spacecraft or by the spacecraft itself prior to use in the navigation system. The neural network determines points in 3 dimensions that provide a 3 dimensional vector as an input to the navigation Kalman Filter. Navigation and attitude determination are performed with a Schmidt Form Unscented Kalman Filter with an IMU as the navigation and attitude. base. NSN can seamlessly incorporate other position and velocity information, such as from communications links or from GPS. The system also includes a neural network for obstacle detection and avoidance. The version shown in the proposal computes a two dimensional trajectories and obstacle by training on sub images using a classification convolutional neural network. The project will explore more sophisticated object identification and tracking.

A laboratory prototype will be built and tested during the project. The navigation system will be demonstrated in an existing high fidelity lunar spacecraft simulation. The simulations will include an end-to-end simulation from LEO to the lunar surface.

The hardware/software combination is small enough for a 6U CubeSat. The software can be used on any spacecraft with a camera for a wide variety of applications. The MATLAB software will be delivered at the end of Phase I for immediate use by NASA engineers. In Phase II Lockheed-Martin, which provided a letter of support, will support the development of flight hardware.

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

This system could be used on the NASA Artemis missions. All NASA satellites above GPS altitude would be potential customers, although MMS did demonstrate that GPS can be used at very high altitudes. Since the system works at all altitudes, and with the addition of the sun sensor throughout the solar system, it could be used on all future NASA missions, even those beyond the edge of the solar system such as the proposed gravitational lens mission. Existing NASA missions could also use the software immediately.

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

The Department of Defense is interested in GPS free navigation. The proposed nuclear thermal DRACO mission would be a potential customer.

Geosynchronous communications satellites are a large potential market. This sensor replaces a star tracker thus does not change the cost of the satellite. Another potential market is LEO satellite constellations. 

Commercial lunar missions are a potential market. 

 

Duration: 6

Form Generated on 06/29/2020 21:11:24