Supervised-machine Learning for Intelligent Collision Avoidance Decision-making and Sensor Tasking

Completed TRL 2 (started at 1, targeting 4)

Description

Building an autonomous architecture that uses directed self-learning neuro-fuzzy networks with the aim of developing an intelligent autonomous collision avoidance decision-making process; including ground and space based sensor tasking and data transfer for on-board autonomous maneuver decision.

Benefits

This will allow for robust autonomous decision making involving the improvement of the initial uncertainties, knowledge of information quality and decision fidelity that will provide potential cost savings for sensor operations, reduce false alarm maneuvers and improved asset protection.

Details

Technology area	GN&C > GN&C Systems Engineering Technologies > GN&C Fault Management, Fault Tolerance, and Autonomy
Program	Center Independent Research & Development: GSFC IRAD (GSFC IRAD)
Lead organization	Goddard Space Flight Center, Greenbelt, MD
Start date	2017-10-01
End date	2018-09-01

Project contacts

Listed on TechPort itself — the most direct way to ask about this specific project.

Jason W Mitchell — jason.w.mitchell@nasa.gov
Timothy D Beach — timothy.d.beach@nasa.gov
Alinda K Mashiku — alinda.k.mashiku@nasa.gov
Carolin Frueh
Nargess Memarsadeghi — nargess.memarsadeghi-1@nasa.gov

How to get involved

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.