000133276 001__ 133276
000133276 005__ 20240410085329.0
000133276 0247_ $$2doi$$a10.1109/IROS47612.2022.9981336
000133276 0248_ $$2sideral$$a132060
000133276 037__ $$aART-2022-132060
000133276 041__ $$aeng
000133276 100__ $$aCano, Lorenzo$$uUniversidad de Zaragoza
000133276 245__ $$aNavigating underground environments using simple topological representations
000133276 260__ $$c2022
000133276 5060_ $$aAccess copy available to the general public$$fUnrestricted
000133276 5203_ $$aUnderground environments are some of the most challenging for autonomous navigation. The long, featureless corridors, loose and slippery soils, bad illumination and unavailability of global localization make many traditional approaches struggle. In this work, a topological-based navigation system is presented that enables autonomous navigation of a ground robot in mine-like environments relying exclusively on a high-level topological representation of the tunnel network. The topological representation is used to generate high-level topological instructions used by the agent to navigate through corridors and intersections. A convolutional neural network (CNN) is used to detect all the galleries accessible to a robot from its current position. The use of a CNN proves to be a reliable approach to this problem, capable of detecting the galleries correctly in a wide variety of situations. The CNN is also able to detect galleries even in the presence of obstacles, which motivates the development of a reactive navigation system that can effectively exploit the predictions of the gallery detection.
000133276 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-105390RB-I00
000133276 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000133276 592__ $$a0.853$$b2022
000133276 593__ $$aComputer Science Applications$$c2022
000133276 593__ $$aSoftware$$c2022
000133276 593__ $$aControl and Systems Engineering$$c2022
000133276 593__ $$aComputer Vision and Pattern Recognition$$c2022
000133276 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000133276 700__ $$0(orcid)0000-0001-7853-3622$$aMosteo, Alejandro R.
000133276 700__ $$0(orcid)0000-0002-7600-0002$$aTardioli, Danilo
000133276 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000133276 773__ $$g2022 (2022), 1717-1724$$pProc. IEEE/RSJ Int. Conf. Intell. Rob. Syst.$$tProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems$$x2153-0858
000133276 8564_ $$s2383508$$uhttps://zaguan.unizar.es/record/133276/files/texto_completo.pdf$$yPostprint
000133276 8564_ $$s3250221$$uhttps://zaguan.unizar.es/record/133276/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000133276 909CO $$ooai:zaguan.unizar.es:133276$$particulos$$pdriver
000133276 951__ $$a2024-04-10-08:38:27
000133276 980__ $$aARTICLE