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Project Overview

BluebirdATC is a Digital Twin and Agent Training Environment for Air Traffic Control.

The project is a collaboration between NATS, The University of Exeter, and the Alan Turing Institute. The goals of the project include:

  • Building a Digital Twin of UK airspace.
  • Developing AI agents that can perform Air Traffic Control (ATC) within this digital twin environment.
  • Safety, trustworthiness and explainability of AI in safety-critical systems such as ATC.

Package Documentation

The BluebirdATC repo is made up of four packages, bluebird-dt, bluebird-api, bluebird-gymnasium, and bluebird-hmi. Click on the boxes in the section below to see the documentation for each one.

General ATC Introduction and Definition of Terms

For a general introduction to ATC and the definition of useful domain-related words and phrases, please visit the Introduction and Glossary page.

Getting Started

Start with the Examples tab in these docs for rendered notebook walkthroughs of the Digital Twin and RL training environment. The source notebooks remain in bluebird-dt/notebooks and bluebird-gymnasium/examples if you want to run or edit them locally.

Running Tests

From the repository root, you can run the workspace Python test suite for bluebird-dt and bluebird-api with:

uv run pytest

To run the same workspace tests in parallel:

uv run pytest -n auto

For package-specific test commands and frontend checks, see the documentation pages for each subproject.

References

  • A Probabilistic Digital Twin of UK Airspace, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.03113
  • A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.03120
  • Human-in-the-Loop Testing of AI Agents for Air Traffic Control with a Regulated Assessment Framework, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.04288
  • Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.03075
  • Online Action-Stacking Improves Reinforcement Learning Performance for Air Traffic Control, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.04287
  • Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach, AIAA SciTech Forum (2026): https://doi.org/10.48550/arXiv.2601.03152
  • A Future Capabilities Agent for Tactical Air Traffic Control, AIAA SciTech Forum (2026): https://arxiv.org/abs/2601.04285
  • Towards Transparent AI Agents for Air Traffic Control, AIAA SciTech Forum (2026): http://dx.doi.org/10.2139/ssrn.6042354