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dc.contributor.authorDeray, Jeremie
dc.contributor.authorMagyar, Bence
dc.contributor.authorSolà Ortega, Joan
dc.contributor.authorAndrade-Cetto, Juan
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2020-04-15T13:59:38Z
dc.date.available2020-04-15T13:59:38Z
dc.date.issued2019
dc.identifier.citationDeray, J. [et al.]. Timed-elastic smooth curve optimization for mobile-base motion planning. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 3143-3149.
dc.identifier.isbn978-1-7281-4004-9
dc.identifier.urihttp://hdl.handle.net/2117/183480
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractThis paper proposes the use of piecewise Cn smooth curve for mobile-base motion planning and control, coined Timed-Elastic Smooth Curve (TESC) planner. Based on a Timed-Elastic Band, the problem is defined so that the trajectory lies on a spline in SE(2) with non-vanishing n-th derivatives at every point. Formulated as a multi-objective nonlinear optimization problem, it allows imposing soft constraints such as collision-avoidance, velocity, acceleration and jerk limits, and more. The planning process is realtime-capable allowing the robot to navigate in dynamic complex scenarios. The proposed method is compared against the state-of-the-art in various scenarios. Results show that trajectories generated by the TESC planner have smaller average acceleration and are more efcient in terms of total curvature and pseudo-kinetic energy while being produced with more consistency than state-of-the-art planners do.
dc.description.sponsorshipThis work was partially supported by the EU H2020 project LOGIMATIC(H2020-Galileo-2015-1-687534), and by the Spanish State Research Agencythrough projects EB-SLAM (DPI2017-89564-P) and the María de Maeztu Seal of Excellence to IRI (MDM-2016-065)
dc.format.extent7 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherCollision avoidance
dc.subject.otherElasticity
dc.subject.otherMobile robots
dc.subject.otherMotion control
dc.subject.otherNonlinear programming
dc.subject.otherSplines (mathematics)
dc.subject.otherTrajectory control
dc.titleTimed-elastic smooth curve optimization for mobile-base motion planning
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents
dc.identifier.doi10.1109/IROS40897.2019.8968240
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Control theory
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8968240
dc.rights.accessOpen Access
local.identifier.drac27029828
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/687534/EU/Tight integration of EGNSS and on-board sensors for port vehicle automation/LOGIMATIC
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/MDM-2016-0656
local.citation.authorDeray, J.; Magyar, B.; Solá, J.; Andrade-Cetto, J.
local.citation.contributorIEEE/RSJ International Conference on Intelligent Robots and Systems
local.citation.publicationName2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
local.citation.startingPage3143
local.citation.endingPage3149


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