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H. Braßel, T. Zeh, H. Fricke
Unmanned Aerial Vehicles (UAV) enable swift and autonomous response to urgent needs, such as search and rescue missions or material delivery. At the same time, airspace restrictions are being established to reduce the external risk of UAV operation considering air and ground risk, which may hinder the efficient usage of UAV in combination with their range-limiting battery capacity. In this study, we present a robust optimization model for a facility location problem of UAV hangars, considering demand hotspots, restricted areas, a standard mission to satisfy battery capacity constraints, and the impact of wind scenarios using water rescue missions as an example. We use open source GIS data to derive positive and negative location factors for UAV hangars and areas of increased risk of drowning as demand points. The pathfinding for the UAV mission uses an A* algorithm to find the shortest mission trajectories in five different restriction scenarios. In addition, binary occupancy grids and image processing algorithms identify restriction-free connections for faster computation. For the optimal UAV hangar locations, we maximize accessibility while minimizing the service time to the demand points showing an improvement of the average service time of 624.20 s for all facility candidates to 401.69 s for one and 315.38 s for two optimal facilities, respectively.
Deutscher Luft- und Raumfahrtkongress 2022, Dresden
Verlag, Ort:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2023
Conference Paper
21,0 x 29,7 cm, 11 Seiten
Stichworte zum Inhalt:
Unmanned Aerial Vehicle, Facility Location Problem, Mission Planning, Restricted Airspace, Search and Rescue
Download - Bitte beachten Sie die Nutzungsbedingungen dieses Dokuments: CC BY-SA 4.0OPEN ACCESS
Braßel, H.; Zeh, T.; Fricke, H. (2023): Fast and Robust Optimization of Unmanned Aerial Vehicle Locations Considering Restricted Areas. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/570387. urn:nbn:de:101:1-2023041411574861233018.
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