https://github.com/oneiben/autonomous-drone-landing-system
an autonomous drone landing system that uses visual and distance-based sensors.
https://github.com/oneiben/autonomous-drone-landing-system
drone image-processing
Last synced: 4 months ago
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an autonomous drone landing system that uses visual and distance-based sensors.
- Host: GitHub
- URL: https://github.com/oneiben/autonomous-drone-landing-system
- Owner: Oneiben
- License: mit
- Created: 2025-02-20T09:53:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-22T14:58:53.000Z (about 1 year ago)
- Last Synced: 2025-04-07T04:54:16.640Z (about 1 year ago)
- Topics: drone, image-processing
- Language: Python
- Homepage:
- Size: 35.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Autonomous Drone Landing System
---
## Table of Contents
- [Autonomous Drone Landing System](#autonomous-drone-landing-system)
- [Table of Contents](#table-of-contents)
- [Overview](#overview)
- [Key Features:](#key-features)
- [Landing Process in Action](#landing-process-in-action)
- [Downward Camera View](#downward-camera-view)
- [Installation](#installation)
- [Project Structure](#project-structure)
- [Usage](#usage)
- [Technologies Used](#technologies-used)
- [Contributing](#contributing)
- [Steps to Contribute:](#steps-to-contribute)
- [License](#license)
---
## Overview
This project focuses on an autonomous drone landing system that uses visual and distance-based sensors. The primary objective is to enable the drone to detect a landing pad with a downward-facing RGB camera and calculate the distance from the ground using a **VL53 distance sensor**. The system leverages advanced image processing and control mechanisms to ensure a safe and accurate landing.
### Key Features:
- **Autonomous Landing Pad Detection:** Utilizes an RGB camera and image processing techniques.
- **Distance Measurement:** Employs a VL53 sensor for real-time altitude assessment.
- **Landing Control:** Integrates Proportional-Integral-Derivative (PID) control for dynamic throttle adjustments and precise positioning.
- **Robust Detection:** Combines YOLO (for object detection), OpenCV (for real-time image processing), and OCR (Tesseract) for identifying the landing pad and its "H" symbol.
- **Simulation Support:** Includes Unity-based simulations for testing and training, compatible with macOS, Linux, and Windows, and leverages `mlagents_envs` for seamless integration of Unity simulations into Python workflows.
---
## Landing Process in Action
Here is example of the landing process captured during testing:
### Downward Camera View
---
## Installation
Follow these steps to set up the project:
1. **Clone the Repository:**
```bash
git clone https://github.com/Oneiben/autonomous-drone-landing-system.git
cd autonomous-drone-landing-system
```
2. **Install Dependencies:**
Make sure you have Python 3.10.12 installed, then install the required packages using:
```bash
pip install -r requirements.txt
```
---
## Project Structure
```plaintext
autonomous-drone-landing-system/
├── Media/
│ ├── landing_progress_gifs/ # GIFs showing the landing process from different angles
│ │ ├── Downward.gif
│ │ └── Main.gif
│ ├── landing_pad_images/ # Images of the landing pad
│ │ ├── landing_pad.png
│ │ └── LandingPad.jpg
├── models/ # YOLO model weights
├── src/ # Python scripts for the landing system and utilities
│ ├── control_actions.py
│ ├── image_processing.py
│ ├── main.py
│ └── simulation.py
├── tests/ # Unit tests for validating components
│ ├── ip_test_cv2.py
│ ├── ip_test_pytesseract.py
│ └── ip_test_yolo.py
├── LICENSE
├── README.md
└── requirements.txt # Python Dependencies
```
---
## Usage
1. **Set up your simulation environment:**
If you want to test in simulation, you can check out this simulation and build a Unity simulation.🔗 [Quadrotor Simulation](https://github.com/Oneiben/quadrotor-simulation-unity.git)
Once you build the Unity simulation, note the path to the build file. You’ll need this path when running the main script.
2. **Launch the main script to start the landing system:**
```bash
python src/main.py
```
3. **Test Detection Methods:**
Use the test scripts in the [📂 tests](./tests/) folder to validate the detection methods with a webcam. YOLO models located in [📂 models](./models/) are used for detecting two landing pads. Ensure the model names align with the images provided in the [📂 landing_pad_images](./Media/landing_pad_images/) directory.
- Example for testing YOLO:
```bash
python tests/ip_test_Yolo.py
```
---
## Technologies Used
- **Python:** Core language for development.
- **YOLO:** Object detection for landing pad recognition.
- **OpenCV:** Image processing library.
- **Tesseract OCR:** Letter detection for identifying the "H" symbol.
- **VL53 Distance Sensor:** Measures altitude.
- **PID Control:** Ensures precise and smooth adjustments for safe landing.
- **Unity:** Used for creating and running drone landing simulations.
- **mlagents_envs:** Provides a `UnityEnvironmentWrapper` for interfacing Python with Unity simulations.
---
## Contributing
Contributions are welcome! If you have suggestions or improvements, feel free to fork the repository and create a pull request.
### Steps to Contribute:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-name
```
3. Commit your changes:
```bash
git commit -m "Description of changes"
```
4. Push the changes and open a pull request.
---
## License
This project is licensed under the MIT License. See the [📜 LICENSE](LICENSE) file for more details.