Inertial parameters identification and joint torques estimation with proximal force/torque sensing
Identifieur interne : 002A94 ( Hal/Curation ); précédent : 002A93; suivant : 002A95Inertial parameters identification and joint torques estimation with proximal force/torque sensing
Auteurs : Silvio Traversaro [Italie] ; Andrea Del Prete [France] ; Serena Ivaldi [France] ; Francesco Nori [Italie]Source :
Abstract
Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.
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key="IT"></country></address><ref type="url">https://www.iit.it/it/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>Italie</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Inertial parameters identification and joint torques estimation with proximal force/torque sensing</title><author role="aut"><persName><forename type="first">Silvio</forename><surname>Traversaro</surname></persName><email></email><idno type="halauthor">1144436</idno><affiliation ref="#struct-105283"></affiliation></author><author role="aut"><persName><forename type="first">Andrea</forename><surname>Del Prete</surname></persName><email>andre.delprete@gmail.com</email><idno type="halauthor">1120690</idno><affiliation ref="#struct-388790"></affiliation></author><author role="aut"><persName><forename type="first">Serena</forename><surname>Ivaldi</surname></persName><email>ivaldi@isir.upmc.fr</email><idno type="idhal">serena-ivaldi</idno><idno type="halauthor">642784</idno><affiliation ref="#struct-262493"></affiliation><affiliation ref="#struct-415996"></affiliation></author><author role="aut"><persName><forename type="first">Francesco</forename><surname>Nori</surname></persName><email></email><idno type="halauthor">314062</idno><affiliation ref="#struct-105283"></affiliation></author><editor role="depositor"><persName><forename>Serena</forename><surname>Ivaldi</surname></persName><email>serena.ivaldi@inria.fr</email></editor><funder ref="#projeurop-98719"></funder></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2015-03-14 13:32:57</date><date type="whenModified">2015-09-22 01:12:16</date><date type="whenReleased">2015-03-23 13:59:58</date><date type="whenProduced">2015-05-26</date><date type="whenEndEmbargoed">2015-03-14</date><ref type="file" target="https://hal.inria.fr/hal-01131610/document"><date notBefore="2015-03-14"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01131610/file/ICRA15_2129_FI.pdf"><date notBefore="2015-03-14"></date></ref></edition><respStmt><resp>contributor</resp><name key="304290"><persName><forename>Serena</forename><surname>Ivaldi</surname></persName><email>serena.ivaldi@inria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01131610</idno><idno type="halUri">https://hal.inria.fr/hal-01131610</idno><idno type="halBibtex">traversaro:hal-01131610</idno><idno type="halRefHtml">2015 IEEE International Conference on Robotics and Automation (ICRA 2015), May 2015, Seattle, WA, United States</idno><idno type="halRef">2015 IEEE International Conference on Robotics and Automation (ICRA 2015), May 2015, Seattle, WA, United States</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LAAS">Laboratoire d'analyse et d'architecture des systèmes</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPT">Institut National Polytechnique de Toulouse</idno><idno type="stamp" n="LAAS-GEPETTO" p="LAAS-ROBOTIQUE">Équipe Mouvement des Systèmes Anthropomorphes</idno><idno type="stamp" n="LAAS-ROBOTIQUE" p="LAAS">LAAS-Robotique</idno><idno type="stamp" n="UNIV-TLSE3">Université Paul Sabatier - Toulouse III</idno><idno type="stamp" n="OPENAIRE">OpenAIRE</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Inertial parameters identification and joint torques estimation with proximal force/torque sensing</title><author role="aut"><persName><forename type="first">Silvio</forename><surname>Traversaro</surname></persName><idno type="halAuthorId">1144436</idno><affiliation ref="#struct-105283"></affiliation></author><author role="aut"><persName><forename type="first">Andrea</forename><surname>Del Prete</surname></persName><email>andre.delprete@gmail.com</email><idno type="halAuthorId">1120690</idno><affiliation ref="#struct-388790"></affiliation></author><author role="aut"><persName><forename type="first">Serena</forename><surname>Ivaldi</surname></persName><email>ivaldi@isir.upmc.fr</email><idno type="idHal">serena-ivaldi</idno><idno type="halAuthorId">642784</idno><affiliation ref="#struct-262493"></affiliation><affiliation ref="#struct-415996"></affiliation></author><author role="aut"><persName><forename type="first">Francesco</forename><surname>Nori</surname></persName><idno type="halAuthorId">314062</idno><affiliation ref="#struct-105283"></affiliation></author></analytic><monogr><idno type="localRef">Rapport LAAS n° 15092</idno><meeting><title>2015 IEEE International Conference on Robotics and Automation (ICRA 2015)</title><date type="start">2015-05-26</date><date type="end">2015-05-30</date><settlement>Seattle, WA</settlement><country key="US">United States</country></meeting><imprint></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="spi.auto">Engineering Sciences [physics]/Automatic</classCode><classCode scheme="halDomain" n="info.info-rb">Computer Science [cs]/Robotics [cs.RO]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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