Autonomous Line Follower Drone using MATLAB & Simulink |MathWorks Mini Drone Competition |Naman Jain

This video showcases a Parrot Mini Drone autonomously tracking a red line and landing on a designated circle using its onboard camera. The project was developed using Simulink MATLAB for the MathWorks International Mini Drone Competition.
Key Features:
Real-time image processing for line detection using edge filters and Hough transforms.
Dynamic path planning with speed adjustments based on object distance.
Obstacle detection through black region analysis.
Autonomous landing using circle detection algorithms.
Project Overview: The drone uses real-time visual data to detect, process, and adjust its path. With features like ROI-based heading adjustments and path correction, the drone avoids obstacles and efficiently follows the path. Challenges like sharp turns and landing accuracy are tackled using advanced trajectory control techniques.
System Requirements:
Hardware: Parrot Mini Drone
Software: MATLAB, Simulink, Computer Vision Toolbox
How it Works:
The camera detects the red line using edge detection and extracts lines via Hough transforms.
Real-time path planning adjusts speed and heading to ensure smooth navigation.
Landing is executed based on circle detection in the designated region.
Watch our simulation demo to see the drone in action!
For more details, check out the repository: github.com/Nam...
Special thanks to MathWorks and our peers for their support!
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Usage Instructions
Drone Setup: Ensure your drone is fully charged and connected to your system.
MATLAB Configuration: Open the MATLAB project. Install required toolboxes.
Parameter Tuning: Adjust camera settings and thresholds based on your drone’s environment.
Run Algorithm: Start the Simulink model to make the drone follow the red line.
Performance Monitoring: Observe the drone’s trajectory and fine-tune parameters.
Optimization: Modify code based on challenges like sharp turns or lighting.
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