Self Landing Rocket System

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Project Overview

The SLRS project aims to revolutionize autonomous rocket landing technology by leveraging advanced control algorithms, machine learning models, and sensor integration. By developing a system that can precisely control a rocket's descent and landing, we seek to improve the safety, reliability, and efficiency of space missions.

Github: https://github.com/ViratSrivastava/SLRS

Key Objectives

  • Autonomous Control: Develop algorithms that allow the rocket to autonomously control its descent and landing in real-time.
  • Precision Landing: Utilize sensor data and machine learning to accurately identify and target the landing site.
  • Robust Integration: Ensure seamless integration of software and hardware components for reliable operation.

Tools & Technologies Used

  • C++: For real-time control algorithms and flight dynamics
  • Python: For data analysis, simulation, and visualization
  • MATLAB: For simulation and modeling of rocket dynamics
  • TensorFlow: For implementing machine learning models
  • Gym: For reinforcement learning environments
  • Blender: For 3D modeling and visualization
  • Git: For version control and collaboration

Development Components

  1. C++ Modules:
    • Real-time control systems
    • Sensor data processing
    • Flight dynamics calculations
  2. Python Implementation:
    • Machine learning models
    • Computer vision systems
    • Data visualization tools
  3. MATLAB Components:
    • System modeling
    • Flight simulations
    • Performance analysis

Current Status

Project is under active development. Core modules for flight control and landing systems are being implemented and tested in simulation environments.

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