Crazyflie Quadcopter Drone Research

By Meredith Coen, FCLC 2023

Crazyflie 2.1 Open Source Quadcopter Drone

When I was beginning to search for an exciting research project to engage with during my junior year, I felt torn between a variety of STEM interests I had developed during my time as an undergraduate student. As a result, I was thrilled to discover Dr. Damian Lyon’s research team in Fordham’s Computer Vision Robotics Lab, where I was able to apply the concepts covered through my mathematics, computer programming, and physics classes in drone research. As a team member during my junior year, I worked to expand the research to analyze the efficacy of gyroscopes and accelerometers in the lightweight Crazyflie 2.0 quadcopter drones. 

More specifically, I analyzed the efficacy of the gyroscopes and accelerometers’ ability in determining the presence of walls at different angles in the vicinity of the flying drone. This required re-running previous experiments and collecting new data, and then altering the Python code to achieve angled flight and data collection. In addition, I worked to have data collected by the gyroscopes and accelerometers in the drones to be classified continually using the RandomForest data classifier, in the hopes to make the classifier generalizable to other Crazyflie 2.0 drones, and other drones in general. Finally, I worked to improve the circumstances of the experiments by configuring and implementing an external Loco-Positioning System (LPS) to track the flight of the drones using GPS technology, as opposed to data collected internally by the drones’ IMUs. While the drone flight was still controlled by Python code, the LPS system allowed additional data to be collected for better detection of flight variation between drone flights, as well as more specific data to be collected about each flight.

During my junior research experience, I was thrilled to learn about and improve my skills in Python coding, data mining, managing drone hardware, and the independent work ethic that is required to be an efficient and productive researcher. I learned the value of being meticulous and the importance of putting in the hours of consistent lab time. In STEM coursework, I have found that the number of hours I put into a project will result in a perfectly balanced amount of productivity and reward. In research, I learned (time and time again) that hours of work may result in only a small fraction of progress towards the end goal. Learning to appreciate the process and embrace the roadblocks is what made me a more effective researcher, beyond garnering greater technical skill or comfort within the lab. When I began the project, I imagined perfect drone flights and expansion of the project threefold. Months in, when I had been stunted for weeks at a time by things like a power outage, a missing comma in thousands of lines of code, or a few faulty software updates of the LPS system, I learned to greatly appreciate one simple drone flight experiment with traceable or random error. I am thrilled to begin my senior year and senior thesis with a better understanding of the work it takes to complete an effective research project and the appropriate scale to take on in order to make a research project meaningful.