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多无人机沿海区域遥感分区与覆盖

Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing.

作者信息

Balampanis Fotios, Maza Iván, Ollero Aníbal

机构信息

Robotics, Vision and Control Group, Universidad de Sevilla, Avda. de los Descubrimientos s/n, 41092 Seville, Spain.

出版信息

Sensors (Basel). 2017 Apr 9;17(4):808. doi: 10.3390/s17040808.

Abstract

This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomposition of the area aids in the partitioning process, which is performed in two steps. In the first step, a growing regions algorithm performs an isotropic partitioning of the area based on the initial locations of the UAVs and their relative capabilities. Then, two novel algorithms are applied to compute an adjustment of this partitioning process, in order to solve deadlock situations that generate non-allocated regions and sub-areas above or below the relative capabilities of the UAVs. Finally, realistic simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions in complex coastal region scenarios under different setups for the UAVs.

摘要

本文针对一组异构无人飞行器(UAV),采用一种考虑机载传感器视场或传感半径的方法,解决沿海区域的精确单元分解和划分问题。基于传感器的区域初始精确单元分解有助于划分过程,该过程分两步进行。第一步,一种生长区域算法根据无人机的初始位置及其相对能力对区域进行各向同性划分。然后,应用两种新颖算法来计算此划分过程的调整,以解决产生未分配区域以及高于或低于无人机相对能力的子区域的死锁情况。最后,针对所提出的解决方案进行了实际模拟评估,所得结果表明,这些算法能够在无人机不同设置的复杂沿海区域场景中计算出有效且合理的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f54/5422169/1f047af5a372/sensors-17-00808-g001.jpg

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