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• W3029501648 abstract "Drone swarms are becoming a new tool for many tasks including surveillance, search, rescue, construction, and defense related activities. As their usage increases, so does the possibility of adversarial attacks on their contribution to these use cases. One possible avenue, whether deliberate or not, is to deny access to the position feedback offered by the Global Positioning System (GPS). Operating in these ‘GPS denied’ environments poses a new challenge; both in navigation, and in collision avoidance. This study proposes two novel concepts; a structural model of environmental deviance to aid in autonomous navigation, and a method to use the output of said model to implement a collision avoidance system. Both of these concepts are developed and tested in the framework of a simulated environment that mimics a GPS-denied scenario. Using data from hundreds of simulated swarm flights, this work shows structured learning can improve navigational accuracy without the need for externally provided position feedback." @default.
• W3029501648 created "2020-06-05" @default.
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• W3029501648 date "2020-01-01" @default.
• W3029501648 modified "2023-09-24" @default.
• W3029501648 title "Autonomous Navigation for Drone Swarms in GPS-Denied Environments Using Structured Learning" @default.
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• W3029501648 doi "https://doi.org/10.1007/978-3-030-49186-4_19" @default.
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