133276 20240410085329.0 doi 10.1109/IROS47612.2022.9981336 sideral 132060 ART-2022-132060 eng Cano, Lorenzo Universidad de Zaragoza Navigating underground environments using simple topological representations 2022 Underground environments are some of the most challenging for autonomous navigation. The long, featureless corridors, loose and slippery soils, bad illumination and unavailability of global localization make many traditional approaches struggle. In this work, a topological-based navigation system is presented that enables autonomous navigation of a ground robot in mine-like environments relying exclusively on a high-level topological representation of the tunnel network. The topological representation is used to generate high-level topological instructions used by the agent to navigate through corridors and intersections. A convolutional neural network (CNN) is used to detect all the galleries accessible to a robot from its current position. The use of a CNN proves to be a reliable approach to this problem, capable of detecting the galleries correctly in a wide variety of situations. The CNN is also able to detect galleries even in the presence of obstacles, which motivates the development of a reactive navigation system that can effectively exploit the predictions of the gallery detection. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MICINN/PID2019-105390RB-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 0.853 2022 Computer Science Applications 2022 Software 2022 Control and Systems Engineering 2022 Computer Vision and Pattern Recognition 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Mosteo, Alejandro R. (orcid)0000-0001-7853-3622 Tardioli, Danilo (orcid)0000-0002-7600-0002 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 2022 (2022), 1717-1724 Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems 2153-0858 2383508 http://zaguan.unizar.es/record/133276/files/texto_completo.pdf Postprint 3250221 http://zaguan.unizar.es/record/133276/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:133276 articulos driver 2024-04-10-08:38:27 ARTICLE