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Space Debris Avoidance & Trajectory Optimization System 🚀🌍


This content originally appeared on DEV Community and was authored by VISWANTHAN S S

This is a submission for the KendoReact Free Components Challenge.

What I Built

With space becoming increasingly congested, avoiding collisions with orbital debris is a significant challenge. To address this, I developed the Space Debris Avoidance & Trajectory Optimization System—an AI-powered solution that predicts space debris movements, optimizes rocket trajectories, and ensures safe mission planning.

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Key Features

  • Real-Time Space Debris Tracking: Utilizes TLE data to track and predict debris movement.
  • Reinforcement Learning-Based Trajectory Optimization: Implements Double Deep Q-Learning (DDQL) to dynamically avoid collisions.
  • Interactive 3D Visualization: Provides trajectory insights with an immersive interface.
  • Flask Web Application: Ensures a seamless, user-friendly experience for mission planners.

Demo

🔗 Live Demo Video
📂 GitHub Repository

🎥 Watch the Live Demo on YouTube:

KendoReact Experience

KendoReact’s Free Components played a crucial role in enhancing the UI/UX of the system. Here’s how I utilized them:

  • Data Grid & Charts: Displayed real-time debris data, mission parameters, and trajectory analytics.
  • Form Components: Enabled intuitive input selection for orbit type, launch window, and trajectory parameters.
  • Modals & Notifications: Provided real-time alerts for potential collision risks.
  • Theming & Custom Styling: Integrated Kendo UI Figma Kits for a clean and professional design.

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Installation Guide

Prerequisites: Ensure you have Python 3.8+ installed.

A. Clone the Repository

git clone https://github.com/thrishankkuntimaddi/Enhanced-Space-Debris-and-Route-Calculation.git
cd Enhanced-Space-Debris-and-Route-Calculation

B. Create a Virtual Environment

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

C. Install Dependencies

pip install -r requirements.txt

D. Run the Simulator

python src/main.py
OR
To launch the web interface:
python src/interface/app.py
Then open http://127.0.0.1:5000/ in your browser.

Aim to Impress

This project integrates Generative AI and Reinforcement Learning to optimize spacecraft paths dynamically. By leveraging AI-powered decision-making, the system continuously improves trajectory selection based on real-world orbital constraints.

Delightfully Designed

I utilized Kendo UI Figma Kits and Progress ThemeBuilder to craft an elegant, intuitive, and engaging user interface. The interactive 3D visualization enhances user experience by providing real-time mission insights.

Future Improvements & Open Contributions

🚀 Next Steps:

  • Enhance RL model accuracy with Transformer-based AI models.
  • Integrate real-time Space-Track API for continuously updated debris tracking.
  • Develop a satellite re-routing module for enhanced mission safety.

I’d love to hear your feedback! Let’s connect, discuss, and collaborate on advancing AI-powered space technology. 🌟

🔗 GitHub Repository
💬 Let’s Connect & Collaborate!


This content originally appeared on DEV Community and was authored by VISWANTHAN S S