Vision-based autonomous UGV detection, tracking, and following for a UAV

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dc.contributor.authorAmil, Fatma G.
dc.contributor.authorSen, Muhammet
dc.contributor.authorKurt, Huseyin Burak
dc.contributor.authorBeycimen, Semih
dc.contributor.authorMillidere, Murat
dc.date.accessioned2024-02-28T14:38:01Z
dc.date.available2024-02-28T14:38:01Z
dc.date.issued2024-01-04
dc.description.abstractThis study proposes a methodology for unmanned ground vehicle (UGV) navigation in off-road environments where GPS signals are not available. The Husky-A200 at Cranfield University, United Kingdom has been used as a UGV in this research project. Due to the limited field of vision of UGVs, a UAV-UGV collaboration approach was adopted. The methodology involves five steps. The first step is divided into three phases: The aerial images of UGV from UAV are generated in the first phase. In the second phase, the UGV is detected and tracked using computer vision techniques. In the third phase, the relative pose (position and heading) between the UAV and UGV is estimated continuously using visual data. In the second step, the UAV maintain a fixed location (position and heading) relative to the UGV. The third step involves capturing aerial images from the UAV‘s mounted camera and transmitting it to the ground station instantly to create a global traversability map that classifies terrain features based on their traversability. In the fourth step, additional sensors such as LiDAR, radar, and IMU are used to refine the global traversability map. In the final step, the UGV navigates automatically using the refined traversability map. This study will focus on the first two steps of the methodology, while subsequent studies will address the remaining steps.en_UK
dc.identifier.citationAmil FG, Sen M, Kurt B, et al., (2024) Vision-based autonomous UGV detection, tracking, and following for a UAV. In: AIAA SCITECH 2024 Forum, 8-12 January 2024, Orlando, USA, Paper number 2024-2093en_UK
dc.identifier.eisbn978-1-62410-711-5
dc.identifier.urihttps://doi.org/10.2514/6.2024-2093
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20898
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectUnmanned Aerial Vehicleen_UK
dc.subjectUnmanned Ground Vehicleen_UK
dc.subjectComputer Visionen_UK
dc.subjectOnboard Sensorsen_UK
dc.subjectTrack Algorithmen_UK
dc.subjectLIDARen_UK
dc.subjectGround Stationen_UK
dc.subjectYaw Controlen_UK
dc.subjectProportional Integral Derivativeen_UK
dc.subjectDeep Learningen_UK
dc.titleVision-based autonomous UGV detection, tracking, and following for a UAVen_UK
dc.typeConference paperen_UK

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