Konferensartikel

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.

Structure maintenance, crack inspection, control systems, robots and automation, autonomous system inspection

[1] A. F. LOTTERMANN, “ Patologias em estruturas de concreto: estudo de caso,” Universidade Regional do Noroeste do Estado do Rio Grande do Sul , Ijuí, 2014.

[2] M. e. a. QUIGLEY, “ROS: an open-source Robot Operating System, p. 5.,” em ICRA workshop on open source software, Kobe, 2009.

[3] T. F. CARRERA, Movimentos reativos no robô turtlebot utilizando o kinect, Bragança: Tese de Doutorado do Instituto Politécnico de Bragança, 2013.

[4] R. L. KLASER, Navegação de veículos autônomos em ambientes externos não estruturados baseada em visão computacional, São Paulo: Tese de Doutorado da Universidade de São Paulo, 2014.

[5] M. e. a. WEBSTER, “Toward reliable autonomous robotic assistants through formal verification: a case study,” IEEE Transactions on Human-Machine Systems, vol. v. 46, n. n. 2, pp. p. 186-196, 2016.

[6] E. e. a. MENENDEZ, “Tunnel structural inspection and assessment using an autonomous robotic system,” Automation in Construction, vol. v. 87, pp. p. 117-126, 2018.

[7] C. ROBERT, “First insights into testing autonomous robot in virtual worlds,” em Software Reliability Engineering Workshops (ISSREW), 2017 IEEE International Symposium on. IEEE, p. 112-115, Toulouse, 2017.

[8] R. G. LINS e S. N. GIVIGI, “Automatic crack detection and measurement based on image analysis,” IEEE Transactions on Instrumentation and Measurement, pp. v. 65, n. 3, p. 583-590, 2016.