<title>Vision algorithms for guiding the automated nondestructive inspector of aging aircraft skins</title>

摘要

Under the FAA Aging Aircraft Research Program we are developing robots to deploy conventional and, later, new-concept NDI sensors for commercial aircraft skin inspection. Our prototype robot, the Automated NonDestructive Inspector (ANDI), holds to the aircraft skin with vacuum assisted suction cups, scans an eddy current sensor, and translates across the aircraft skin via linear actuators. Color CCD video cameras are used to align the robot with a series of rivets we wish to inspect using NDI inspection sensors. In a previous paper we provided a background scenario and described two different solutions to the alignment problem: a model-based system built around edge detection and a trainable neural network system. In this paper, we revisit the background and previous research and detail the first steps taken towards a method that will combine the neural and the model based systems: a neural edge detector.