Anti-Poaching Drone Control

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dc.contributor.authorLyman, Matthewen
dc.contributor.authorHudson, Matthewen
dc.contributor.authorBishop, Coryen
dc.date.accessioned2022-05-12T03:06:10Zen
dc.date.available2022-05-12T03:06:10Zen
dc.date.issued2022-05-11en
dc.description.abstractOur 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.en
dc.description.notesAntiPoachingDroneControlReport.pdf — A PDF report of our team's work amongst the SeaQL Lab's larger Anti-Poaching Drone Control project. AntiPoachingDroneControlReport.docx — An editable Microsoft Word document of our team's work report. AntiPoachingDroneControlPresentation.pdf — The final presentation given to our CS4624: Multimedia, Hypertext, and Information Access class to summarize our work, challenges, and lessons learned throughout the project. AntiPoachingDroneControlPresentation.pptx — An editable Microsoft PowerPoint version of our final presentation.en
dc.description.sponsorshipMarine Management Organisationen
dc.description.sponsorshipVirginia Tech Center for Coastal Studiesen
dc.identifier.urihttp://hdl.handle.net/10919/110055en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectpoachingen
dc.subjectdroneen
dc.subjectautonomousen
dc.subjectsharken
dc.subjectChagosen
dc.subjectconservationen
dc.subjectimage analysisen
dc.subjectAIen
dc.subjectJetson Nanoen
dc.subjectROSen
dc.subjectRobotic Operating Systemen
dc.subjecthexacopteren
dc.titleAnti-Poaching Drone Controlen
dc.typePresentationen
dc.typeReporten

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CS4624: Multimedia, Hypertext, and Information Access