Machine Learning for Airspace Complexity Management

Completed TRL 3 (started at 2, targeting 3)

Description

We will explore a range of independent variables and metrics that affect air traffic complexity, identify multi- agent reinforcement learning formulations of the problem, and identify anomaly detection and precursor identification algorithms that can identify and predict phase transitions in air traffic complexity

Benefits

A tool that can identify and predict phase transitions in airspace complexity, suggest actions that will avoid the increased complexity, and maintain efficiency and safety in a way that scales with increasing numbers of Unmanned Aerial Vehicles (UAVs).

Details

Technology area	Software, Modeling, Simulation, and Information Processing > Information Processing and Artificial Intelligence > Intelligent Data Understanding
Program	Center Innovation Fund: ARC CIF (ARC CIF)
Lead organization	Ames Research Center, Moffett Field, CA
Start date	2020-10-01
End date	2021-09-30

Project contacts

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

Nikunj C Oza — nikunj.c.oza@nasa.gov

How to get involved

This is early/mid-stage (TRL 3) — 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.