000129789 001__ 129789
000129789 005__ 20240410085606.0
000129789 0247_ $$2doi$$a10.1177/0278364919882082
000129789 0248_ $$2sideral$$a115835
000129789 037__ $$aART-2019-115835
000129789 041__ $$aeng
000129789 100__ $$0(orcid)0000-0002-0492-6471$$aPalacios-Gasós, J.M.
000129789 245__ $$aEquitable persistent coverage of non-convex environments with graph-based planning
000129789 260__ $$c2019
000129789 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129789 5203_ $$aIn this article, we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring every point to be constantly revisited. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared with other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.
000129789 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/C076-2014$$9info:eu-repo/grantAgreement/ES/DGA/T04-FSE$$9info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-098719-B-I00$$9info:eu-repo/grantAgreement/ES/MICIU/RTC-2017-5965-6$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/DPI2015-69376-R$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2014-1847-6$$9info:eu-repo/grantAgreement/ES/UZ/CUD2017-18
000129789 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000129789 590__ $$a4.703$$b2019
000129789 591__ $$aROBOTICS$$b5 / 28 = 0.179$$c2019$$dQ1$$eT1
000129789 592__ $$a3.212$$b2019
000129789 593__ $$aApplied Mathematics$$c2019$$dQ1
000129789 593__ $$aArtificial Intelligence$$c2019$$dQ1
000129789 593__ $$aSoftware$$c2019$$dQ1
000129789 593__ $$aMechanical Engineering$$c2019$$dQ1
000129789 593__ $$aModeling and Simulation$$c2019$$dQ1
000129789 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ1
000129789 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000129789 700__ $$0(orcid)0000-0002-7600-0002$$aTardioli, D.
000129789 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, E.$$uUniversidad de Zaragoza
000129789 700__ $$0(orcid)0000-0002-3032-954X$$aSagüés, C.$$uUniversidad de Zaragoza
000129789 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000129789 773__ $$g38, 14 (2019), 1674-1694$$pInt. j. rob. res.$$tInternational Journal of Robotics Research$$x0278-3649
000129789 8564_ $$s11048733$$uhttps://zaguan.unizar.es/record/129789/files/texto_completo.pdf$$yPostprint
000129789 8564_ $$s2578156$$uhttps://zaguan.unizar.es/record/129789/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000129789 909CO $$ooai:zaguan.unizar.es:129789$$particulos$$pdriver
000129789 951__ $$a2024-04-10-08:42:58
000129789 980__ $$aARTICLE