Space tracker
# 🚀 SpaceGuard AI: Autonomous Orbital Debris Mitigation System
🛰️ *AI-Powered Space Sustainability Solution*
**Built for NASA Space Apps Challenge: Impact Edition**
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## 📖 Executive Summary
**SpaceGuard AI** is an **AI-driven orbital debris mitigation platform** crafted for **proactive space sustainability initiatives**.
It integrates:
- **Autonomous multi-modal drone fleets**
- **Reinforcement learning-based control systems**
- **LSTM-powered predictive analytics**
Together, these technologies actively **detect, track, and capture hazardous orbital debris**, securing operational safety for satellites and future space missions.
🎯 *Precisely tailored to align with NASA’s Orbital Debris Mitigation Programs.*
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## 🚀 Key Highlights
✅ **Autonomous Multi-Modal Drones**
- Net, harpoon, and magnetic capture mechanisms
✅ **Realistic Orbital Debris Dynamics Simulation**
- J2 perturbations, atmospheric drag, orbital decay
✅ **LSTM-Based Predictive Trajectory Forecasting**
- AI-predicts debris positions for optimized intercept paths
✅ **Voice-Controlled Command System via NLP**
- Mission control via natural language commands
✅ **Real-Time 3D Debris Tracking and Visualization**
- Live debris tracking through interactive 3D simulations
✅ **Judge-Friendly Presentation Approach**
- Clean, video-optional, interactive documentation
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## 🌟 Key Differentiators
### 🔹 1️⃣ Multi-Agent Reinforcement Learning Control System
| **Module** | **Technology** | **Outcome** |
|:----------------------|:----------------------------------|:----------------------------------------------|
| Drone Navigation | Proximal Policy Optimization (PPO) | Optimizes debris capture path planning |
| Collision Avoidance | Potential Field Algorithm | Prevents inter-drone and debris collisions |
| Target Prioritization | Custom Danger Scoring Engine | Focuses on high-risk, high-priority debris |
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### 🔹 2️⃣ NASA Orbital Debris Data Integration
- **Fetches and processes live orbital debris data** from **CelesTrak TLE sets**
- Enables **mission-critical real-time awareness**
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## 📦 Sample Python Integration
```python
# Real TLE data ingestion from CELESTRAK
import requests
def load_debris_from_tle():
response = requests.get("https://celestrak.org/NORAD/elements/active.txt")
tle_data = response.text.splitlines()
print(f"Loaded {len(tle_data)} orbital objects.")
```
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## 🌐 Why SpaceGuard AI Matters
🚀 With over **36,500 tracked objects in orbit** and thousands of smaller, untracked debris threatening missions daily — **SpaceGuard AI** delivers an **autonomous, intelligent, and scalable solution**.
By integrating:
- **AI-based prediction**
- **Reinforcement learning**
- **Real-time simulation**
- **Voice-commanded operations**
It ensures a **safe, reliable, and efficient space traffic management future**.
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## ✨ Built for NASA. Designed for Space. Powered by AI.
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## 👨💻 About the Creator
**👤 Name:** Sheikh Haris Raza Sheikh Ilyas
**📅 Date of Birth:** 21/02/2007
**📍 Address:** Plot No 13, Tal Nagar, Bidgaon, Nagpur, Maharashtra - 440035
**🎓 Education:** Pursuing Electrical Engineering at **KDK College of Engineering, Nagpur**
💡 *Though my primary field is Electrical Engineering, I’m deeply passionate about programming and futuristic AI innovations.*
Languages I work with:
- **C**
- **C++**
- **Java**
- **Python**
**🔗 LinkedIn:** [https://www.linkedin.com/in/haris-sheikh-15b287317](https://www.linkedin.com/in/haris-sheikh-15b287317)
**🔗 GitHub:** [https://github.com/Sheikhharis311](https://github.com/Sheikhharis311)
*This project is a reflection of my vision to merge engineering, AI, and space-tech to solve real-world problems.*
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## 📌 Follow for more AI and SpaceTech projects soon!
**#SpaceGuardAI #NASAAppsChallenge #AIforSpace #OrbitalDebrisMitigation #ReinforcementLearning #MadeByHaris**
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