Kevin Lam is a master student in Data Science at the University of Sydney. He completed his bachelor degree in economics in Switzerland at the University of Lausanne in 2019. For his capstone project last semester, he worked with a team of data science students to create a context-aware deep learning framework which can predict mobile app usage from IoT data. He enjoy learning about any aspects and usage of data science and has a particular interest in computer vision and data mining. His other hobbies involve fpv drone piloting and bboying.
Swarm-based Drone Delivery Dataset
Drones are unmanned aircrafts that operate with various degrees of autonomy. The wide availability of drones opens opportunities for a wide number of applications including package delivery. Drones for delivery present some unique challenges to fully deliver on their potential. In particular, drones have limited payload and battery capacity. There are instances where there is a need to deliver goods by a deadline and which weigh more than the maximum of a single drone’s payload. In this case, the use of drone swarms is an effective alternative to address the aforementioned constraints for the timely delivery of heavier and/or multiple packages which go beyond the capability of one single drone. Drone swarms are teams of autonomous unmanned aerial vehicles that act as a collective entity. The goal of the project is to set up a drone swarm using available drones (DJI Edu Tello) and collect a dataset using different formations of swarms and varying wind speeds and directions.