Understanding animal behaviour at large spatial and temporal scales is crucial for ecology and conservation, but it is difficult to monitor wild animals continuously without interfering with them. Traditional approaches often rely on GPS collars, body-mounted sensors, or stationary camera traps, which are hard to scale and can affect the animals.<\/p>\n\n\n\n
Led by Aamir Ahmad at the University of Stuttgart\u2019s Institute of Flight Mechanics and Flight Control, the WildCap<\/strong> project (2021\u20132026) developed autonomous aerial robots that can detect, track, and follow animals in their natural habitat. Using onboard cameras and AI, the system estimates behaviour, 3D pose, and body shape without attaching any sensors or markers to the animals. <\/p>\n\n\n\n WildCap demonstrates how autonomous robots and AI can enable non-invasive, large-scale monitoring of animal behaviour and movement. This could support wildlife conservation, ecological research, and veterinary applications by providing continuous, high-resolution data without disturbing the animals. The project\u2019s methods for multi-robot coordination, 3D pose estimation, and real-time individual identification may also inform other domains, such as environmental monitoring and search-and-rescue. <\/p>\n\n\n\nKey results<\/h2>\n\n\n\n
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Why it matters<\/h2>\n\n\n\n
This project produced the following peer-reviewed publications:\u00a0<\/h2>\n\n\n\n
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