Anti-Poaching Drone Control
Our project assists the SeaQL Lab of Virginia Tech's Department of Fisheries and Wildlife Conservation. Working with the Marine Management Organisation of the UK, the Lab's project entails developing an autonomous drone swarm that can fly predetermined routes around the Chagos Archipelago and send alerts about potential poaching boats, based on machine learning image analysis in the drones' attached computing modules. The main goal of this project is to save the sharks and the ecosystem of those waters while decreasing the time, money, and effort for the local Coast Guard to perform regular monitoring. Instead, the drones will send detection alerts to a remote server being monitored by a ranger if it spots a potential poaching boat. Our report details our contributions to the overall project.
Our team took responsibility for several smaller tasks integral to the overall project. First, we familiarized ourselves with the Robotic Operating System (ROS) to connect, calibrate, test, and record video using the cameras provided. ROS will control much of the drones' added functionality such as running the poaching boat detection algorithm, sending flight commands to the drones, and streaming video over a cellular connection. Next, we aided the larger project team in repairing one off-the-shelf drone for potential flight testing. After unsuccessful troubleshooting, we moved to help finish construction of the primary hexacopter. Finally, we wrote a script to start the 4G cellular connection automatically when a drone is powered on.
The AntiPoachingDroneControlReport details this work amidst the larger project goals of the SeaQL Lab. The AntiPoachingDroneControlPresentation gives a brief summary of our project work and the lessons learned. This was presented to our CS4624: Multimedia, Hypertext, and Information Access class to summarize our project work and experiences.