We present a robust approach for detecting defects on an aircraft electrical wiring interconnection system in order to comply with the safety regulations such as the forbidden interference and allowed bend radius of cables and/or harness in mechanical assemblies. For this purpose, we exploit 3-D point clouds acquired with a 3-D scanner and the 3-D computer-aided design (CAD) model of the assembly being inspected. Our method mainly consists of two processes: an offline automatic selection of informative viewpoints and an online automatic treatment of the acquired 3-D point cloud from said viewpoints. The viewpoint selection is based on the 3-D CAD model of the assembly and the calculation of a scoring function, which evaluates a set of candidate viewpoints. After the offline viewpoint selection is completed, the robotic inspection system is ready for operation. During the online inspection phase, a 3-D point cloud is analyzed for measuring the bend radius of each cable and its minimum distance to the other elements in the assembly. For this, we developed a 3-D segmentation algorithm to find the cables in the point cloud, by modeling a cable as a collection of cylinders. Using the segmented cable, we carried out a quantitative analysis of the interference and bend radius of each cable. The performance of the inspection system is validated on synthetic and real data, the latter being acquired by our precalibrated robotic system. Our dataset is acquired by scanning different zones of an aircraft engine. The experimental results show that our proposed approach is accurate and promising for industrial applications.
Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated.
In this paper, we address the problem of automatic robotic inspection in two parts: first, automatic selection of informative viewpoints before the inspection process is started, and, second, automatic treatment of the acquired 3D point cloud from said viewpoints. We apply our system to detecting defects on aircraft Electrical Wiring Interconnection System (EWIS) in order to comply with the growing amount of safety regulations such as interference and allowable bend radius of cables in mechanical assemblies.
This paper deals with an automated preflight aircraft inspection using a pan-tilt-zoom camera mounted on a mobile robot moving autonomously around the aircraft. The general topic is image processing framework for detection and exterior inspection of different types of items, such as closed or unlatched door, mechanical defect on the engine, the integrity of the empennage, or damage caused by impacts or cracks. The detection step allows to focus on the regions of interest and point the camera toward the item to be checked. It is based on the detection of regular shapes, such as rounded corner rectangles, circles, and ellipses. The inspection task relies on clues, such as uniformity of isolated image regions, convexity of segmented shapes, and periodicity of the image intensity signal. The approach is applied to the inspection of four items of Airbus A320: oxygen bay handle, air-inlet vent, static ports, and fan blades. The results are promising and demonstrate the feasibility of an automated exterior inspection.
This paper deals with the inspection of an airplane using a Pan-Tilt-Zoom camera mounted on a mobile robot moving around the airplane. We present image processing methods for detection and inspection of four different types of items on the airplane exterior. Our detection approach is focused on the regular shapes such as rounded corner rectangles and ellipses, while inspection relies on clues such as uniformity of isolated image regions, convexity of segmented shapes and periodicity of the image intensity signal. The initial results are promising and demonstrate the feasibility of the envisioned robotic system.