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基于无人机的接触式检测的室内路径规划算法

Indoor Path-Planning Algorithm for UAV-Based Contact Inspection.

作者信息

González de Santos Luis Miguel, Frías Nores Ernesto, Martínez Sánchez Joaquín, González Jorge Higinio

机构信息

CINTECX, GeoTECH Group, Campus Universitario de Vigo, University of Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain.

GeoTECH Group, Department Natural Resources and Environmental Engineering, Campus Lagoas, School of Aerospace Engineering, University of Vigo, 32004 Ourense, Spain.

出版信息

Sensors (Basel). 2021 Jan 18;21(2):642. doi: 10.3390/s21020642.

Abstract

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8-9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

摘要

如今,无人机被广泛用于多种用途,如基础设施检查或监视。本文提出了一种室内环境下的实时路径规划算法,旨在使用无人机执行接触式检查任务。该算法唯一使用的输入是无人机将要导航的建筑物的点云。该算法分为两个主要部分。第一部分是预处理算法,用于处理点云,将其分割成房间并对每个房间进行离散化。第二部分是必须实时执行的路径规划算法。通过这种方式,所有的计算负载都在第一步,即预处理步骤中,从而使路径计算算法更快。该方法已在不同建筑物中进行了测试,测量了不同路径计算的执行时间。如结果部分所示,所开发的算法能够在8 - 9毫秒内计算出一条新路径。所开发的算法满足执行时间限制,并且已被证明在路线计算方面是可靠的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9520/7831516/8268bf3d29af/sensors-21-00642-g001.jpg

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