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Improved situation awareness for autonomous taxiing through self-learning
journal contribution
posted on 2016-05-12, 14:06 authored by Bowen Lu, Matthew CoombesMatthew Coombes, Baibing LiBaibing Li, Wen-Hua ChenWen-Hua ChenAs 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.
Funding
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.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
IEEE Transactions On Intelligent Transportation SystemsVolume
17Issue
12Pages
3553 - 3564Citation
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.Publisher
IEEEVersion
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/Acceptance date
2016-04-21Publication date
2016-05-18Notes
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ISSN
1524-9050eISSN
1558-0016Publisher version
Language
- en