000168182 001__ 168182
000168182 005__ 20260212204405.0
000168182 0247_ $$2doi$$a10.1109/LRA.2022.3194686
000168182 0248_ $$2sideral$$a130104
000168182 037__ $$aART-2022-130104
000168182 041__ $$aeng
000168182 100__ $$aMorilla-Cabello, David$$uUniversidad de Zaragoza
000168182 245__ $$aSweep-Your-Map: efficient coverage planning for aerial teams in large-scale environments
000168182 260__ $$c2022
000168182 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168182 5203_ $$aThe efficiency of path-planning in robot navigation is crucial in tasks such as search-and-rescue and disaster surveying, but this is emphasized even more when considering multirotor aerial robots due to the limited battery and flight time. In this spirit, this work proposes an efficient, hierarchical planner to achieve comprehensive visual coverage of large-scale outdoor scenarios for small drones. Following an initial reconnaissance flight, a coarse map of the scene gets built in real-time. Then, regions of the map that were not appropriately observed are identified and grouped by a novel perception-aware clustering process that enables the generation of continuous trajectories (sweeps) to cover them efficiently. Thanks to this partitioning of the map into a set of tasks, we can generalize the planning to an arbitrary number of drones and perform a well-balanced workload distribution among them. We compare our approach against a state-of-theart method for exploration and show the advantages of our pipeline in terms of efficiency for obtaining coverage in large environments.
000168182 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000168182 590__ $$a5.2$$b2022
000168182 591__ $$aROBOTICS$$b10 / 30 = 0.333$$c2022$$dQ2$$eT2
000168182 592__ $$a1.693$$b2022
000168182 593__ $$aArtificial Intelligence$$c2022$$dQ1
000168182 593__ $$aBiomedical Engineering$$c2022$$dQ1
000168182 593__ $$aComputer Science Applications$$c2022$$dQ1
000168182 593__ $$aMechanical Engineering$$c2022$$dQ1
000168182 593__ $$aControl and Optimization$$c2022$$dQ1
000168182 593__ $$aControl and Systems Engineering$$c2022$$dQ1
000168182 593__ $$aHuman-Computer Interaction$$c2022$$dQ1
000168182 593__ $$aComputer Vision and Pattern Recognition$$c2022$$dQ1
000168182 594__ $$a7.6$$b2022
000168182 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000168182 700__ $$aBartolomei, Luca
000168182 700__ $$aTeixeira, Lucas
000168182 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, Eduardo$$uUniversidad de Zaragoza
000168182 700__ $$aChli, Margarita
000168182 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000168182 773__ $$g7, 4 (2022), 10810-10817$$pIEEE Robot. autom. let.$$tIEEE Robotics and Automation Letters$$x2377-3766
000168182 8564_ $$s4996449$$uhttps://zaguan.unizar.es/record/168182/files/texto_completo.pdf$$yPostprint
000168182 8564_ $$s3216540$$uhttps://zaguan.unizar.es/record/168182/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000168182 909CO $$ooai:zaguan.unizar.es:168182$$particulos$$pdriver
000168182 951__ $$a2026-02-12-20:41:30
000168182 980__ $$aARTICLE