Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93053
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Title: Proactive guidance for accurate UAV landing on a dynamic platform : a visual-inertial approach
Authors: Chang, CW 
Lo, LY 
Cheung, HC 
Feng, YR 
Yang, AS
Wen, CY 
Zhou, WF
Issue Date: Jan-2022
Source: Sensors (Switzerland), Jan. 2022, v. 22, no. 1, 404
Abstract: This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.
Keywords: UAV
VTOL
Object tracking
Deep learning
Sensor fusion
Kalman filter
Autonomous landing
Optimal trajectory
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors (Switzerland) 
ISSN: 1424-8220
DOI: 10.3390/s22010404
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Chang, C.-W.; Lo, L.-Y.; Cheung, H.C.; Feng, Y.; Yang, A.-S.; Wen, C.-Y.; Zhou,W. Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach. Sensors 2022, 22, 404 is available at https://doi.org/10.3390/s22010404.
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