Aegis

Space Debris Collision Avoidance & Orbital Intelligence

Securing Low Earth Orbit with real-time astrodynamics, predictive machine learning, and autonomous fuel-optimized evasive maneuvers.

Explore Live Demo   •   Architecture   •   Installation

🌌 Overview

With Low Earth Orbit (LEO) becoming exponentially congested with active payloads and defunct space debris, traditional manual collision monitoring cannot scale. Aegis is an end-to-end, real-time Space Situational Awareness (SSA) and autonomous collision avoidance platform.

By ingesting publicly available Two-Line Element (TLE) datasets, Aegis dynamically computes high-precision orbital state vectors, evaluates collision probabilities ($P_c$), and leverages machine learning to recommend Delta-V optimized evasive maneuvers.

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✨ Core Capabilities

🌐 Cinematic 3D Visualizer

• Data-Driven Orbits: High-fidelity 3D Earth model rendering dynamically propagated orbits rather than static mock paths.
• Visual Classification: Intuitive color-coding for payloads (Yellow), debris (Red), and rocket bodies (Green).
• Live Telemetry: WebSocket-driven tracking canvas rendering real-time object positions and velocity vectors at 60 FPS.

⚡ Astrodynamics & Telemetry

• SGP4 Ephemeris Engine: Mathematical core converting TLE elements into instantaneous Cartesian $(X, Y, Z)$ state vectors.
• Persistent Streaming: Bi-directional WebSocket pipeline (/ws/live) for continuous client-side updates without polling overhead.

🎯 Collision Probability Engine

• Risk Triage: Automated categorization of conjunction events based on computed Probability of Collision ($P_c$).
• Mathematical Sandbox: Integrated 2D Foster and Monte Carlo calculator supporting custom covariance matrices ($\sigma_x, \sigma_y, \sigma_z$) and hard-body radii.
• Covariance Ellipsoids: Visual spatial error representations at the predicted Time of Closest Approach (TCA).

🎛️ Autonomous Maneuver Planning

• Delta-V Optimization: AI-assisted recommendations calculating minimal impulse burns required to clear safety thresholds.
• Command Authorization: Interactive review drawer allowing operators to simulate burn vectors and queue authorization payloads.

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🛠️ Technology Stack

Component	Technologies
Frontend & UI	Vanilla JS (SPA), CSS3 Glassmorphism, Font Awesome
3D Rendering	Three.js, WebGL, Custom Camera Controllers
Astrodynamics	SGP4 Propagation Model, Ephemeris Computing
Calculations / ML	2D Foster, Monte Carlo, XGBoost / RL Optimization
Data Ingestion	Space-Track.org API, CelesTrak TLE Feeds
Infrastructure	Node.js / Python Backend, Render Cloud Deployment

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🏗️ System Architecture

Click to expand Data Pipeline Diagram

┌────────────────────────────────────────────────────────┐
│             External Orbital Data Sources              │
│       (Space-Track.org / CelesTrak NORAD APIs)         │
└───────────────────────────┬────────────────────────────┘
                            │ TLE Stream
                            ▼
┌────────────────────────────────────────────────────────┐
│                   Aegis Core Backend                   │
│  ┌────────────────────────┐  ┌──────────────────────┐  │
│  │     SGP4 Propagator    │  │ Conjunction Analysis │  │
│  └───────────┬────────────┘  └──────────┬───────────┘  │
│              ▼                          ▼              │
│  ┌──────────────────────────────────────────────────┐  │
│  │  AI / RL Autonomous Maneuver Recommendation Engine │  │
│  └──────────────────────────┬───────────────────────┘  │
└─────────────────────────────┼──────────────────────────┘
                              │
             ┌────────────────┴────────────────┐
             │ REST API (`/api`) & WebSockets  │
             └────────────────┬────────────────┘
                              ▼
┌────────────────────────────────────────────────────────┐
│              Aegis Mission Control (SPA)               │
│  ┌───────────────────────┐  ┌───────────────────────┐  │
│  │   Three.js 3D Engine  │  │  Interactive Sandbox  │  │
│  └───────────────────────┘  └───────────────────────┘  │
└────────────────────────────────────────────────────────┘

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💻 Getting Started

1. Clone the repository

git clone [https://github.com/your-username/aegis.git](https://github.com/your-username/aegis.git)
cd aegis

2. Serve Locally

Because Aegis relies on native ES Modules and fetch requests, it must be run through a local web server (opening the .html file directly may cause CORS/module errors).

Using Python:

python -m http.server 8080

Using Node.js:

npx http-server . -p 8080

3. Launch

Navigate to http://localhost:8080 in your web browser.

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📡 API Reference

View REST & WebSocket Endpoints

Method	Endpoint	Description
GET	/api/catalog	Fetch filtered orbital object catalog with active TLEs.
GET	/api/conjunctions	Retrieve prioritized list of predicted high-risk close approaches.
POST	/api/calculator/pc	Compute $P_c$ using custom covariance and miss distance.
POST	/api/maneuvers/accept	Authorize and queue an optimized evasive burn.
WS	/ws/live	Persistent stream for real-time propagated coordinates.

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⚖️ License & Acknowledgements

• License: Distributed under the MIT License.
• Data Providers: Sincere gratitude to Space-Track.org and CelesTrak for providing open-access TLE orbital datasets.
• Research: Inspired by the ESA Space Debris Office for open-access conjunction analysis research and benchmarks.

_{Built for the future of orbital sustainability.}