https://github.com/orionop/anchor
An autonomous system for landing a quadcopter on a moving target using computer vision, sensor fusion, and PID control.
https://github.com/orionop/anchor
cv pid quadcopter-control
Last synced: 3 months ago
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An autonomous system for landing a quadcopter on a moving target using computer vision, sensor fusion, and PID control.
- Host: GitHub
- URL: https://github.com/orionop/anchor
- Owner: orionop
- License: mit
- Created: 2025-01-12T14:25:46.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-16T11:10:52.000Z (3 months ago)
- Last Synced: 2025-02-16T12:19:18.843Z (3 months ago)
- Topics: cv, pid, quadcopter-control
- Language: Python
- Homepage:
- Size: 19.5 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Autonomous Quadcopter Landing System (Anchor)
An autonomous system for landing a quadcopter on a moving target using computer vision, sensor fusion, and PID control. The system integrates real-time data from cameras, IMUs, and GPS to guide the quadcopter to a stable landing on a moving target.
## Project Overview
This project demonstrates an autonomous system that allows a quadcopter to land on a moving target. The system uses sensors and vision algorithms to track the target and adjust the quadcopter's position in real time for a precise landing.
## Tech Stack
- **Programming Languages**: Python, C++
- **Simulation**: Gazebo, ROS (Robot Operating System)
- **Libraries**: OpenCV, NumPy, SciPy, PID Control
- **Microcontroller**: Raspberry Pi
- **Sensors**: IMU (MPU6050), Camera (Pi Camera), GPS
- **Flight Control**: Pixhawk, ArduPilot
- **Communication**: MAVLink (for communication with Pixhawk)
- **Path Planning**: A* Algorithm for optimal target tracking
## Features
- **Real-time target tracking** using a camera and OpenCV.
- **Sensor Fusion** with IMU and GPS for stable flight.
- **PID Controller** to stabilize the quadcopter during landing.
- **Simulation Environment** in Gazebo with ROS integration.
- **Moving Target Simulation** in Gazebo to replicate real-world conditions.
#### **Installation Instructions**
1. Clone the repository:
```git clone https://github.com/yourusername/autonomous-quadcopter-landing-system.git```
2. Install dependencies:
``` pip install -r requirements.txt```
3. Set up ROS environment (if using ROS):
```source /opt/ros/noetic/setup.bash```
4. Set up Gazebo and ROS packages:
``` cd ~/catkin_ws catkin_make source devel/setup.bash```
5. Run the simulation:
``` roslaunch quadcopter_landing.launch```
## Usage
Once the simulation runs, you can control the quadcopter’s flight and landing by modifying the parameters in the configuration files or adjusting the PID controller values.
## How to Run
1. Install required libraries:
```pip install -r requirements.txt```
2. Launch Gazebo and ROS:
```roslaunch quadcopter_landing.launch```
#### **Directory Structure**
```plaintext
autonomous-quadcopter-landing-system/
├── docs/
│ └── overview.md
├── src/
│ ├── flight_control/
│ │ └── pid_controller.py
│ ├── vision/
│ │ └── target_tracking.py
│ └── sensor_fusion/
│ └── imu_gps_fusion.py
├── launch/
│ └── quadcopter_landing.launch
├── sim/
│ └── gazebo_worlds/
│ └── moving_target.world
├── requirements.txt
├── README.md
└── LICENSE