Devon Super

Drones use computer vision machine learning techniques to navigate their environments. Modern Graph Neural Network designs enable vastly improved perception for drones, but few systems exist to deploy this work outside of simulation. The goal of this project was to create a framework based on ROS that implements the communication necessary for Graph Neural Networks. The framework was composed of several customizable programs built on an industry-standard robotics framework. Preliminary testing was conducted by running the framework with an advanced depth estimation network on drones during flight tests. Across both autonomous and human-controlled flight, message passing between two drones was successful. Computational strain and network strain were measured at manageable levels. The framework was demonstrated to be effective in real robotics applications, with successful communication occurring during drone flight. Implementation with a modern Graph Neural Network could enable real-world perception that exceeds present-day capabilities. The framework bridges the gap between modern computer science research and real-life deployment.

1st place in Engineering & Technology semifinals JSHS New York-Metro 2024

2nd place  finalist in NY Metro JSHSn 2024

Oral Presenter for JSHS Nationals 2024

Terra NYC STEM Fair semifinalist 2024, finalist 2023

My Research Paper