MathWorks Global Student Drone Challenge 2025

Competition: MathWorks Global Student Drone Challenge
Result: 3rd Place (2025)

This project develops an autonomous drone navigation system for the MathWorks Global Student Drone Challenge, implementing advanced vision-based control algorithms for precise flight path optimization and autonomous landing capabilities.

Project Overview

The research focuses on developing a comprehensive autonomous navigation solution for Parrot Mambo drones, combining computer vision techniques with advanced control systems to achieve optimal performance in competitive drone racing environments.

Key Achievements

• Vision-Based Control Implementation: Programmed sophisticated vision-based control algorithms for Parrot Mambo using dynamic masking techniques, ray-tracing algorithms, and closed-loop yaw control for precise navigation and obstacle avoidance.

• Adaptive Flight Optimization: Integrated intelligent speed variation algorithms and zone-based automatic landing systems to minimize track completion time while maintaining flight stability and safety.

• Competition Performance: Developed and tested the complete autonomous navigation stack within the competitive timeframe, demonstrating reliable performance in challenging flight scenarios.

Technical Stack

Development Environment: MATLAB, Simulink