Trajectory generation for unmanned aerial manipulators through quadratic programming

In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order...

Descripción completa

Detalles Bibliográficos
Autores: Rossi, Roberto, Santamaria-Navarro, Àngel, Andrade-Cetto, Juan, Rocco, Paolo
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/166448
Acceso en línea:http://hdl.handle.net/10261/166448
Access Level:acceso abierto
Palabra clave:Aerial manipulation
Trajectory generation
Mobile manipulation
Aerial robotics
id ES_d0e7bb2004dacf9b3ada49a43612bb5a
oai_identifier_str oai:digital.csic.es:10261/166448
network_acronym_str ES
network_name_str España
repository_id_str
spelling Trajectory generation for unmanned aerial manipulators through quadratic programmingRossi, RobertoSantamaria-Navarro, ÀngelAndrade-Cetto, JuanRocco, PaoloAerial manipulationTrajectory generationMobile manipulationAerial roboticsIn this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.Their work has been partially funded by the EU project AEROARMS H2020-ICT2014-1-644271 and by the Spanish Ministry of Economy and Competitiveness project ROBINSTRUCT TIN2014-58178-R.Peer ReviewedInstitute of Electrical and Electronics EngineersEuropean CommissionMinisterio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2018201820172018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/166448reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/644271info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2014-58178-Rhttps://doi.org/10.1109/LRA.2016.2633625Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1664482026-05-22T06:33:51Z
dc.title.none.fl_str_mv Trajectory generation for unmanned aerial manipulators through quadratic programming
title Trajectory generation for unmanned aerial manipulators through quadratic programming
spellingShingle Trajectory generation for unmanned aerial manipulators through quadratic programming
Rossi, Roberto
Aerial manipulation
Trajectory generation
Mobile manipulation
Aerial robotics
title_short Trajectory generation for unmanned aerial manipulators through quadratic programming
title_full Trajectory generation for unmanned aerial manipulators through quadratic programming
title_fullStr Trajectory generation for unmanned aerial manipulators through quadratic programming
title_full_unstemmed Trajectory generation for unmanned aerial manipulators through quadratic programming
title_sort Trajectory generation for unmanned aerial manipulators through quadratic programming
dc.creator.none.fl_str_mv Rossi, Roberto
Santamaria-Navarro, Àngel
Andrade-Cetto, Juan
Rocco, Paolo
author Rossi, Roberto
author_facet Rossi, Roberto
Santamaria-Navarro, Àngel
Andrade-Cetto, Juan
Rocco, Paolo
author_role author
author2 Santamaria-Navarro, Àngel
Andrade-Cetto, Juan
Rocco, Paolo
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Aerial manipulation
Trajectory generation
Mobile manipulation
Aerial robotics
topic Aerial manipulation
Trajectory generation
Mobile manipulation
Aerial robotics
description In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/166448
url http://hdl.handle.net/10261/166448
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/644271
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2014-58178-R
https://doi.org/10.1109/LRA.2016.2633625
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420215682465792
score 15,81155

Similares en LA Referencia