Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind.
Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind.
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DOI：
10.3390/s18072132
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发表时间：
2018-07-03
期刊：
影响因子：
--
通讯作者：
Liu C
中科院分区：
文献类型：
--
作者：
Coombes M;Fletcher T;Chen WH;Liu C
本文提出了一种规划固定翼无人机（UAV）航测覆盖路径的新方法。该方法不是以前文献中提出的更通用的覆盖路径规划技术，而是专门致力于减少固定翼飞机测量的飞行时间。这是通过三个方面实现的：通过在测量飞行时间模型中添加风，考虑到固定翼飞机不限于在感兴趣区域的多边形内飞行的事实，以及将该区域分解为有利于快速飞行时间的凸多边形的智能方法。结果表明，风可以对测量时间产生巨大影响，垂直飞行可以带来飞行时间优势。小型无人机的空速非常慢，很容易在风速为 50% 的风中飞行。这就是为什么该技术被证明如此有效，因为忽略小型、缓慢的固定翼飞机的风是一个相当大的疏忽。将该方法与之前在随机多边形上使用蒙特卡罗模拟的技术进行比较，结果表明飞行时间显着减少。
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.
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影响因子：
1.6
作者：
KEIL, JM
通讯作者：
KEIL, JM
DOI：
10.1007/bf00977785
发表时间：
1980-01-01
期刊：
INTERNATIONAL JOURNAL OF COMPUTER & INFORMATION SCIENCES
影响因子：
--
作者：
LEE, DT;SCHACHTER, BJ
通讯作者：
SCHACHTER, BJ
DOI：
10.1016/b978-044482537-7/50012-7
发表时间：
2000-01-01
期刊：
HANDBOOK OF COMPUTATIONAL GEOMETRY
影响因子：
--
作者：
Keil, JM
通讯作者：
Keil, JM
影响因子：
5
作者：
Bendig, Juliane;Bolten, Andreas;Bareth, Georg
通讯作者：
Bareth, Georg
影响因子：
3.3
作者：
Cai, Guowei;Chen, Ben M.;Dong, Miaobo
通讯作者：
Dong, Miaobo