000168112 001__ 168112
000168112 005__ 20260126155510.0
000168112 0247_ $$2doi$$a10.1002/rob.70157
000168112 0248_ $$2sideral$$a147681
000168112 037__ $$aART-2026-147681
000168112 041__ $$aeng
000168112 100__ $$aCano, Lorenzo$$uUniversidad de Zaragoza
000168112 245__ $$aAutonomous navigation in large‐scale underground environments based on a purely topological understanding of tunnel networks
000168112 260__ $$c2026
000168112 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168112 5203_ $$aThis work presents a non‐geometrical navigation approach based on a purely topological understanding of underground environments. By conceptualizing subterranean scenarios as a set of tunnels that intersect with each other, and taking a navigation approach based on topological instructions, we simplify the navigation problem to the sequential execution of human‐understandable instructions. This approach is built on top of a lightweight Convolutional Neural Network (CNN) that processes the readings of a 3D LiDAR sensor and produces an estimation of the angular positions of the surrounding tunnels with respect to the robot. As a result of this approach, our method can navigate these underground environments by only being provided with the necessary topological instructions, without the need for a map, or for building one during navigation. Additionally, it can also rely on a lightweight graph representation of the environment. This graph can be either defined by the user, generated during navigation or explicitly built in an exploration task. To showcase these capabilities, this article provides an experimental evaluation of the method both in simulation and in a real environment.
000168112 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00
000168112 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168112 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168112 700__ $$0(orcid)0000-0002-7600-0002$$aTardioli, Danilo
000168112 700__ $$0(orcid)0000-0001-7853-3622$$aMosteo, Alejandro R.$$uUniversidad de Zaragoza
000168112 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000168112 773__ $$g(2026), 1-22$$pJournal of Field Robotics$$tJournal of Field Robotics$$x1556-4959
000168112 8564_ $$s8026117$$uhttps://zaguan.unizar.es/record/168112/files/texto_completo.pdf$$yVersión publicada
000168112 8564_ $$s2399163$$uhttps://zaguan.unizar.es/record/168112/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168112 909CO $$ooai:zaguan.unizar.es:168112$$particulos$$pdriver
000168112 951__ $$a2026-01-26-14:50:45
000168112 980__ $$aARTICLE