Tzitzilonis Vasileios, Malandrakis Konstantinos, Zanotti Fragonara Luca, Gonzalez Domingo Jose Angel, Avdelidis Nicolas P, Tsourdos Antonios, Forster Kevin
Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Cranfield MK43 0AL, UK.
Centre for Structures, Assembly and Intelligent Automation, Cranfield MK43 0AL, UK.
Sensors (Basel). 2019 Apr 17;19(8):1824. doi: 10.3390/s19081824.
In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects' detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects' detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects.
在大型民用飞机制造中,机翼面板的无损检测是一个耗时的后期制作过程。这项工作旨在应对这一挑战,并通过使用小型现成多旋翼无人机进行自动空中检测来改进缺陷检测。该无人机配备了一个宽视场摄像头和一个紫外线手电筒,用于进行非侵入式成像检测。特别是,无人机被编程执行完整任务,并将视频实时传输到地面控制站,在那里执行缺陷检测算法。为了在飞机机翼检测过程中使用路径跟踪方法评估系统行为,在MATLAB/SIMULINK中对所提出的平台进行了数学建模。此外,还实现了两种缺陷检测算法,并在一个包含在空中客车公司设施检查期间获得的图像的数据集上进行了测试。结果表明,对于当前数据集,所提出的方法可以识别所有包含缺陷的图像。