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Open AccessArticle

Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind

Department of Automotive and Aeronautical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2132; https://doi.org/10.3390/s18072132
Received: 7 June 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 3 July 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
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. View Full-Text
Keywords: dynamic programming; remote sensing; polygon decomposition; coverage path planning (CPP); Boustrophedon paths; fixed wing UAV dynamic programming; remote sensing; polygon decomposition; coverage path planning (CPP); Boustrophedon paths; fixed wing UAV
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Coombes, M.; Fletcher, T.; Chen, W.-H.; Liu, C. Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind. Sensors 2018, 18, 2132.

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