Estimation of Soft Tissue Mechanical Parameters from Robotic Manipulation Data
Identifieur interne : 001749 ( Pmc/Checkpoint ); précédent : 001748; suivant : 001750Estimation of Soft Tissue Mechanical Parameters from Robotic Manipulation Data
Auteurs : Pasu Boonvisut ; Russell Jackson ; M. Cenk Çavu O LuSource :
- IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation [ 2152-4092 ] ; 2012.
Abstract
Robotic motion planning algorithms used for task automation in robotic surgical systems rely on availability of accurate models of target soft tissue’s deformation. Relying on generic tissue parameters in constructing the tissue deformation models is problematic; because, biological tissues are known to have very large (inter- and intra-subject) variability. A priori mechanical characterization (e.g., uniaxial bench test) of the target tissues before a surgical procedure is also not usually practical. In this paper, a method for estimating mechanical parameters of soft tissue from sensory data collected during robotic surgical manipulation is presented. The method uses force data collected from a multiaxial force sensor mounted on the robotic manipulator, and tissue deformation data collected from a stereo camera system. The tissue parameters are then estimated using an inverse finite element method. The effects of measurement and modeling uncertainties on the proposed method are analyzed in simulation. The results of experimental evaluation of the method are also presented.
Url:
DOI: 10.1109/ICRA.2012.6225071
PubMed: 24683498
PubMed Central: 3966138
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Estimation of Soft Tissue Mechanical Parameters from Robotic
Manipulation Data</title><author><name sortKey="Boonvisut, Pasu" sort="Boonvisut, Pasu" uniqKey="Boonvisut P" first="Pasu" last="Boonvisut">Pasu Boonvisut</name></author><author><name sortKey="Jackson, Russell" sort="Jackson, Russell" uniqKey="Jackson R" first="Russell" last="Jackson">Russell Jackson</name></author><author><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24683498</idno><idno type="pmc">3966138</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966138</idno><idno type="RBID">PMC:3966138</idno><idno type="doi">10.1109/ICRA.2012.6225071</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">001825</idno><idno type="wicri:Area/Pmc/Curation">001825</idno><idno type="wicri:Area/Pmc/Checkpoint">001749</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Estimation of Soft Tissue Mechanical Parameters from Robotic
Manipulation Data</title><author><name sortKey="Boonvisut, Pasu" sort="Boonvisut, Pasu" uniqKey="Boonvisut P" first="Pasu" last="Boonvisut">Pasu Boonvisut</name></author><author><name sortKey="Jackson, Russell" sort="Jackson, Russell" uniqKey="Jackson R" first="Russell" last="Jackson">Russell Jackson</name></author><author><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name></author></analytic><series><title level="j">IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation</title><idno type="ISSN">2152-4092</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Robotic motion planning algorithms used for task automation in robotic
surgical systems rely on availability of accurate models of target soft
tissue’s deformation. Relying on generic tissue parameters in
constructing the tissue deformation models is problematic; because, biological
tissues are known to have very large (inter- and intra-subject) variability. A
priori mechanical characterization (e.g., uniaxial bench test) of the target
tissues before a surgical procedure is also not usually practical. In this
paper, a method for estimating mechanical parameters of soft tissue from sensory
data collected during robotic surgical manipulation is presented. The method
uses force data collected from a multiaxial force sensor mounted on the robotic
manipulator, and tissue deformation data collected from a stereo camera system.
The tissue parameters are then estimated using an inverse finite element method.
The effects of measurement and modeling uncertainties on the proposed method are
analyzed in simulation. The results of experimental evaluation of the method are
also presented.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101518638</journal-id><journal-id journal-id-type="pubmed-jr-id">38753</journal-id><journal-id journal-id-type="nlm-ta">IEEE Int Conf Robot Autom</journal-id><journal-id journal-id-type="iso-abbrev">IEEE Int Conf Robot Autom</journal-id><journal-title-group><journal-title>IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation</journal-title></journal-title-group><issn pub-type="ppub">2152-4092</issn></journal-meta><article-meta><article-id pub-id-type="pmid">24683498</article-id><article-id pub-id-type="pmc">3966138</article-id><article-id pub-id-type="doi">10.1109/ICRA.2012.6225071</article-id><article-id pub-id-type="manuscript">NIHMS495959</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Estimation of Soft Tissue Mechanical Parameters from Robotic
Manipulation Data</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Boonvisut</surname><given-names>Pasu</given-names></name><email>bpasu@case.edu</email></contrib><contrib contrib-type="author"><name><surname>Jackson</surname><given-names>Russell</given-names></name><email>rcj33@case.edu</email></contrib><contrib contrib-type="author"><name><surname>Çavuşoğlu</surname><given-names>M. Cenk</given-names></name><email>cavusoglu@case.edu</email></contrib><aff id="A1">Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff></contrib-group><pub-date pub-type="nihms-submitted"><day>18</day><month>7</month><year>2013</year></pub-date><pub-date pub-type="ppub"><day>31</day><month>12</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>26</day><month>3</month><year>2014</year></pub-date><volume>2012</volume><fpage>4667</fpage><lpage>4674</lpage><pmc-comment>elocation-id from pubmed: 10.1109/ICRA.2012.6225071</pmc-comment>
<abstract><p id="P1">Robotic motion planning algorithms used for task automation in robotic
surgical systems rely on availability of accurate models of target soft
tissue’s deformation. Relying on generic tissue parameters in
constructing the tissue deformation models is problematic; because, biological
tissues are known to have very large (inter- and intra-subject) variability. A
priori mechanical characterization (e.g., uniaxial bench test) of the target
tissues before a surgical procedure is also not usually practical. In this
paper, a method for estimating mechanical parameters of soft tissue from sensory
data collected during robotic surgical manipulation is presented. The method
uses force data collected from a multiaxial force sensor mounted on the robotic
manipulator, and tissue deformation data collected from a stereo camera system.
The tissue parameters are then estimated using an inverse finite element method.
The effects of measurement and modeling uncertainties on the proposed method are
analyzed in simulation. The results of experimental evaluation of the method are
also presented.</p></abstract></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Boonvisut, Pasu" sort="Boonvisut, Pasu" uniqKey="Boonvisut P" first="Pasu" last="Boonvisut">Pasu Boonvisut</name><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name><name sortKey="Jackson, Russell" sort="Jackson, Russell" uniqKey="Jackson R" first="Russell" last="Jackson">Russell Jackson</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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