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Optimal polygon decomposition for UAV survey coverage path planning in wind

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journal contribution
posted on 10.07.2018 by Matthew Coombes, Tom Fletcher, Wen-Hua Chen, Cunjia Liu
In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time.

Funding

This research was funded by the Science and Technology Facilities Council (STFC) under the Newton Fund with Grant Number ST/N006852/1.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Sensors

Volume

18

Issue

7

Pages

2132 - 2132

Citation

COOMBES, M. ... et al, 2018. Optimal polygon decomposition for UAV survey coverage path planning in wind. Sensors, 18 (7), 2132.

Publisher

MDPI © The Authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

28/06/2018

Publication date

2018-07-03

Notes

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

eISSN

1424-8220

Language

en

Licence

Exports