Self-supervised obstacle detection during autonomous UAS taxi operations
| dc.contributor.author | Shaikh, Yousuf | |
| dc.contributor.author | Petrunin, Ivan | |
| dc.contributor.author | Zolotas, Argyrios | |
| dc.date.accessioned | 2023-01-26T13:14:56Z | |
| dc.date.available | 2023-01-26T13:14:56Z | |
| dc.date.issued | 2023-01-19 | |
| dc.description.abstract | This 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.citation | Shaikh 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, USA | en_UK |
| dc.identifier.eisbn | 978-1-62410-699-6 | |
| dc.identifier.uri | https://doi.org/10.2514/6.2023-2672 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19020 | |
| dc.language.iso | en | en_UK |
| dc.publisher | AIAA | en_UK |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.title | Self-supervised obstacle detection during autonomous UAS taxi operations | en_UK |
| dc.type | Conference paper | en_UK |