Self-supervised obstacle detection during autonomous UAS taxi operations

dc.contributor.authorShaikh, Yousuf
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorZolotas, Argyrios
dc.date.accessioned2023-01-26T13:14:56Z
dc.date.available2023-01-26T13:14:56Z
dc.date.issued2023-01-19
dc.description.abstractThis research explores the application of self-supervised learning techniques for obstacle detection and collision avoidance during UAS auto-taxi. Autoencoders were used to detect obstacles as anomalies by comparison of reconstruction errors. RGB cameras and millimetre wave radars covering conflict free zones (CFZs) around the own-ship were chosen to provide inputs to autoencoders. Results demonstrated that autoencoders were able to detect obstacles as anomalies within the CFZs but with certain limitations at lay the foundations of further work and investigation within the research area.en_UK
dc.identifier.citationShaikh MY, Petrunin I, Zolotas A. (2023) Self-supervised obstacle detection during autonomous UAS taxi operations. In: AIAA SciTech 2023 Forum, 23-27 January 2023, National Harbor, Maryland, USAen_UK
dc.identifier.eisbn978-1-62410-699-6
dc.identifier.urihttps://doi.org/10.2514/6.2023-2672
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19020
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleSelf-supervised obstacle detection during autonomous UAS taxi operationsen_UK
dc.typeConference paperen_UK

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