Cislunar Space Traffic Management System (CSTMS)

A comprehensive system for managing space traffic in cislunar space using Physics-Informed Neural Networks (PINNs) for orbit determination and collision avoidance.

Features

• Physics-Informed Orbit Determination: Uses PINNs to accurately predict spacecraft trajectories while respecting physical constraints
• Real-time Collision Detection: Monitors and predicts potential collisions between spacecraft
• Avoidance Maneuver Planning: Generates optimal avoidance maneuvers when collisions are predicted
• Interactive 3D Visualization: Beautiful and intuitive visualization of spacecraft trajectories and collision warnings
• User-Friendly Interface: Streamlit-based web interface for easy interaction with the system

Installation

1. Clone the repository:

git clone https://github.com/yourusername/cstms.git
cd cstms

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Usage

1. Start the application:

streamlit run app.py

2. Open your web browser and navigate to the URL shown in the terminal (usually http://localhost:8501)

3. Use the system:

   • Upload observation data in MPC format using the sidebar
   • Click "Process Data" to train the PINN model
   • Adjust the prediction window and collision detection parameters as needed
   • View trajectories and collision warnings in the main display
   • Generate avoidance maneuvers when warnings are detected

Data Format

The system accepts observation data in Minor Planet Center (MPC) 80-column format. Example:

00001    C2019 04 20.1234  11 22 33.44 +12 34 56.7          12.3 R      568

You can also use custom format files with columns: time, RA (degrees), DEC (degrees).

System Components

• app.py: Main application file with Streamlit interface
• pinn_model.py: Implementation of the Physics-Informed Neural Network
• dynamics.py: Physical models for orbital dynamics
• collision_detection.py: Collision detection and avoidance algorithms
• visualization.py: 3D visualization tools using Plotly
• data_utils.py: Data loading and preprocessing utilities

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Research paper: "Physics-Informed Orbit Determination for Cislunar Space Applications"
• PyTorch team for the deep learning framework
• Plotly team for the visualization library
• Streamlit team for the web interface framework