000074981 001__ 74981 000074981 005__ 20200117221643.0 000074981 0247_ $$2doi$$a10.1177/0278364918775520 000074981 0248_ $$2sideral$$a107513 000074981 037__ $$aART-2018-107513 000074981 041__ $$aeng 000074981 100__ $$aLorente, M.T. 000074981 245__ $$aModel-based robocentric planning and navigation for dynamic environments 000074981 260__ $$c2018 000074981 5060_ $$aAccess copy available to the general public$$fUnrestricted 000074981 5203_ $$aThis work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented. 000074981 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T04$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-76676-R 000074981 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000074981 590__ $$a6.134$$b2018 000074981 591__ $$aROBOTICS$$b4 / 26 = 0.154$$c2018$$dQ1$$eT1 000074981 592__ $$a2.189$$b2018 000074981 593__ $$aApplied Mathematics$$c2018$$dQ1 000074981 593__ $$aArtificial Intelligence$$c2018$$dQ1 000074981 593__ $$aSoftware$$c2018$$dQ1 000074981 593__ $$aMechanical Engineering$$c2018$$dQ1 000074981 593__ $$aModeling and Simulation$$c2018$$dQ1 000074981 593__ $$aElectrical and Electronic Engineering$$c2018$$dQ1 000074981 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000074981 700__ $$aOwen, E. 000074981 700__ $$0(orcid)0000-0002-0449-2300$$aMontano, L.$$uUniversidad de Zaragoza 000074981 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000074981 773__ $$g37, 8 (2018), 867-889$$pInt. j. rob. res.$$tInternational Journal of Robotics Research$$x0278-3649 000074981 8564_ $$s5166456$$uhttps://zaguan.unizar.es/record/74981/files/texto_completo.pdf$$yVersión publicada 000074981 8564_ $$s102271$$uhttps://zaguan.unizar.es/record/74981/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000074981 909CO $$ooai:zaguan.unizar.es:74981$$particulos$$pdriver 000074981 951__ $$a2020-01-17-22:05:30 000074981 980__ $$aARTICLE