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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/21209

Title: Improved situation awareness for autonomous taxiing through self-learning
Authors: Lu, Bowen
Coombes, Matthew
Li, Baibing
Chen, Wen-Hua
Keywords: Autonomous taxiing
Situation awareness
Unmanned aerial vehicle
Issue Date: 2017
Publisher: IEEE
Citation: LU, B. ...et al., 2017. Improved situation awareness for autonomous taxiing through self-learning. IEEE Transactions On Intelligent Transportation Systems, 17 (12), pp.3553-3564.
Abstract: As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs’ autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. Indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.
Description: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor: This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
Version: Published
DOI: 10.1109/TITS.2016.2557588
URI: https://dspace.lboro.ac.uk/2134/21209
Publisher Link: http://dx.doi.org/10.1109/TITS.2016.2557588
ISSN: 1524-9050
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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