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Improved situation awareness for autonomous taxiing through self-learning

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journal contribution
posted on 12.05.2016, 14:06 by Bowen Lu, Matthew Coombes, Baibing Li, Wen-Hua Chen
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.

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 Systems

Volume

17

Issue

12

Pages

3553 - 3564

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.

Publisher

IEEE

Version

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

21/04/2016

Publication date

2016-05-18

Notes

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

ISSN

1524-9050

eISSN

1558-0016

Language

en

Licence

Exports