Our team designed, built, and tested three flight-based projects: a model rocket engineered in OpenRocket and physically launched, an autonomous drone programmed in Python to navigate to waypoints and react to color using onboard sensors, and a rubber band-powered foam airplane. Each project centered on core principles of flight stability and iterative testing, from calculating center of gravity versus center of pressure on the rocket to layering and verifying autonomous behaviors in the drone's software.

This project focused on programming and debugging a drone to improve its autonomous and manual control. We tested enhanced field-oriented driving, color detection, and a custom P-loop controller to make the drone more accurate and stable. By the end of the week, the drone was better tuned, but it still could not complete the full final course.

This project was about building and testing a small rubber-band-powered airplane. We focused on improving the plane’s balance, propeller design, and launch performance so it could fly farther and more smoothly. The final flights reached about 24.5 feet, showing that the design worked well, with a few remaining factors affecting consistency.

Our team designed a model rocket in OpenRocket, physically built it from that design, and launched it to compare its actual flight performance against the simulated predictions.

This project examined air quality at different levels of parking garages by collecting measurements such as PM1, PM2.5, PM10, temperature, humidity, and CO₂. We found that the top levels generally had cleaner air, while ground level had more pollution because of vehicle traffic and weaker airflow. We then used MATLAB to organize the data and build a model that predicted PM2.5 accurately from the other measurements.