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Trajectory planning for communication relay unmanned aerial vehicles in urban dynamic environments

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journal contribution
posted on 2017-02-06, 15:05 authored by Pawel Ladosz, Hyondong Oh, Wen-Hua ChenWen-Hua Chen
This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, heading rates and speeds.

Funding

This work was supported by the UK Engineering and Physical Science Research Council (EPSRC) under the Grant EP/J011525/1 and the Research Fund (Project Number 1.160089) of UNIST (Ulsan National Institute of Science and Technology).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Journal of Intelligent & Robotic Systems

Citation

LADOSZ, P., OH, H. and CHEN, W.-H., 2018. Trajectory planning for communication relay unmanned aerial vehicles in urban dynamic environments. Journal of Intelligent & Robotic Systems, 89(1-2), pp. 7-25.

Publisher

© Springer

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2017-01-14

Publication date

2017-01-28

Notes

This is a post-peer-review, pre-copyedit version of an article published in Journal of Intelligent & Robotic Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10846-017-0484-y.

ISSN

0921-0296

eISSN

1573-0409

Language

  • en