000075046 001__ 75046
000075046 005__ 20200716101514.0
000075046 0247_ $$2doi$$a10.1007/s10514-018-9783-9
000075046 0248_ $$2sideral$$a107427
000075046 037__ $$aART-2019-107427
000075046 041__ $$aeng
000075046 100__ $$aAlonso-Mora, J.
000075046 245__ $$aDistributed multi-robot formation control in dynamic environments
000075046 260__ $$c2019
000075046 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075046 5203_ $$aThis paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.
000075046 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/DPI2015-69376-R
000075046 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000075046 590__ $$a3.602$$b2019
000075046 591__ $$aROBOTICS$$b7 / 28 = 0.25$$c2019$$dQ1$$eT1
000075046 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b39 / 136 = 0.287$$c2019$$dQ2$$eT1
000075046 592__ $$a1.502$$b2019
000075046 593__ $$aArtificial Intelligence$$c2019$$dQ1
000075046 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000075046 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, E.$$uUniversidad de Zaragoza
000075046 700__ $$aNägeli, T.
000075046 700__ $$aHilliges, O.
000075046 700__ $$aSchwager, M.
000075046 700__ $$aRus, D.
000075046 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000075046 773__ $$g43, 5 (2019), 1079–1100$$pAuton. robots$$tAUTONOMOUS ROBOTS$$x0929-5593
000075046 8564_ $$s3593576$$uhttps://zaguan.unizar.es/record/75046/files/texto_completo.pdf$$yVersión publicada
000075046 8564_ $$s9684$$uhttps://zaguan.unizar.es/record/75046/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000075046 909CO $$ooai:zaguan.unizar.es:75046$$particulos$$pdriver
000075046 951__ $$a2020-07-16-09:21:25
000075046 980__ $$aARTICLE