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Inspection of defects in civil infrastructure has been a constant field of research. The enlargement of a crack, along the time, can increase the deterioration of the structure, which can result in slight problems on the material's surface until, in the serious case, the rupture of the concrete structure. In the current operational paradigm, a technician is responsible to go physically to the field in order to measure cracks in the structures. However, in terms of efficiency, the manual inspection presents many problems, such as the low accuracy of measurements taken in the field and problems for accessing high-rises, narrows places, nuclear plants, among others. Hence, the current project developed at the Federal University of ABC aims to develop a fully autonomous system capable to automate the crack measurement detection and measurement process. The proposed system uses a set of robots capable to navigate and process data by itself, ground and aerial platforms, machine vision algorithms embedded into a processing device and a remote station in order to manage all the tasks to be done. In the current proposal, the ground platform adopted is the Turtlebot 2 ®, which uses an embedded computer to process the programs. The aerial platform to be adopted is an eight-engine drone octocopter that will make use of the flight controller Pixhawk 2.1 ®. Both of them will be integrated and controlled from the network interconnection of several programs, such as mapping, navigation, and image processing programs. Thus, simulations will be performed by using the Gazebo ® program and the Robot Operating System ®, wherein the system will be exposed to the real situations to evaluate the system efficiency. As result, once the autonomous system detects cracks, the embedded vision system algorithm must be able to process the image and assess the type and the damage caused to the inspected structure.

Proposal of an Autonomous System for Inspection of Structures