Introduction

Welcome to the AeroHack Resources tab. This page is here to help you move fast and keep your work reproducible. AeroHack is an advanced aerospace build sprint: you’re building a unified planning framework that demonstrates both (1) aircraft mission planning + simulation and (2) spacecraft mission planning (LEO observation + downlink scheduling).
Use the links and suggestions below to pick reliable tools, avoid common pitfalls, and ship a clean, testable submission.

Tools and Technologies

You may use any stack, but submissions are easiest to judge when they are reproducible and lightweight.

Recommended baseline

  • Python 3 (recommended for judging/reproducibility)

  • Jupyter Notebook (optional) or a single runnable script/CLI

  • Git + GitHub/GitLab for version control and sharing

Useful Python libraries (optional)

  • Data & math: numpy, scipy, pandas

  • Visualisation: matplotlib

  • Geometry / geofencing: shapely

  • Optimisation / planning:

    • ortools (constraint programming / routing)

    • pyomo (optimisation modelling)

    • cvxpy (convex optimisation)

  • Graph / routing: networkx

  • Orbital / astronomy:

    • poliastro / astropy (orbit mechanics tooling)

    • skyfield / sgp4 (TLE-based propagation and pass timing)

Engineering quality (strongly recommended)

 

  • A requirements.txt (or equivalent)

  • A clear README with “Reproduce Results” steps

  • Basic tests or sanity checks (even minimal)

Inspiration

If you’re stuck on what “good” looks like, aim for outcomes that resemble real ops work:

Aircraft side

  • Route planning under wind + endurance + no-fly zones

  • Risk-aware routing (penalise flying near restricted regions)

  • Robust planning (Monte-Carlo wind seeds; success rate)

Spacecraft side

  • A 7-day schedule that balances:

    • target observations (with time windows)

    • downlinks during ground station contacts

    • simplified pointing/power feasibility

  • Mission value accounting (only count observations that are successfully downlinked)

  • Clear feasibility checks (no impossible slews, no power “bankrupt” schedules)

System-level (what makes submissions “advanced”)

 

  • One consistent way to define constraints + objective + solver/heuristic

  • Good validation and honest limitations

  • Reproducibility that a judge can run without guessing

Contact Us & Support Channels

For questions, issues, or clarifications:

  • Email (public): flightalertpro884@gmail.com

  • Devpost: Use the hackathon page Updates and comments for official announcements and Q&A.

  • Bug reports / clarifications: If you found an ambiguity in the challenge, email with:

    • your team name

    • what you were trying to do

    • the exact question or conflict

    • (optional) a screenshot/error snippet

Response times may vary, so plan around not needing last-minute rule changes.

Copy/paste links below as needed (documentation + free learning references):

Python

https://docs.python.org/3/

 

NumPy

https://numpy.org/doc/

 

SciPy

https://docs.scipy.org/doc/scipy/

 

pandas

https://pandas.pydata.org/docs/

 

Matplotlib

https://matplotlib.org/stable/

 

Shapely (geofencing / polygons)

https://shapely.readthedocs.io/

 

Google OR-Tools (routing / constraint solving)

https://developers.google.com/optimization

 

Pyomo (optimisation modelling)

https://pyomo.readthedocs.io/

 

CVXPY (convex optimisation)

https://www.cvxpy.org/

 

NetworkX (graphs / routing)

https://networkx.org/documentation/stable/

 

Astropy (astronomy/orbits tools)

https://docs.astropy.org/

 

poliastro (orbit mechanics in Python)

https://docs.poliastro.space/

 

Skyfield (TLE/ephemeris, passes)

https://rhodesmill.org/skyfield/

 

sgp4 (TLE propagation)

https://pypi.org/project/sgp4/

 

Fundamentals reference (free book): Orbital Mechanics for Engineering Students (look for library access)

https://www.sciencedirect.com/book/9780080977478/orbital-mechanics-for-engineering-students

https://github.com/Prithiraz/aerohack-starter.git