Conference paper Open Access

Augmented Hierarchical Quadratic Programming for Adaptive Compliance Robot Control

Tassi, Francesco; De Momi, Elena; Ajoudani, Arash


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  <identifier identifierType="DOI">10.5281/zenodo.4663753</identifier>
  <creators>
    <creator>
      <creatorName>Tassi, Francesco</creatorName>
      <givenName>Francesco</givenName>
      <familyName>Tassi</familyName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
    <creator>
      <creatorName>De Momi, Elena</creatorName>
      <givenName>Elena</givenName>
      <familyName>De Momi</familyName>
      <affiliation>Politecnico di Milano:</affiliation>
    </creator>
    <creator>
      <creatorName>Ajoudani, Arash</creatorName>
      <givenName>Arash</givenName>
      <familyName>Ajoudani</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1261-737X</nameIdentifier>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Augmented Hierarchical Quadratic Programming for Adaptive Compliance Robot Control</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-05-29</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4663753</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4663752</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020-sophia</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Today&amp;#39;s robots are expected to fulfill different requirements originated from executing complex tasks in uncertain environments, often in collaboration with humans. To deal with this type of multi-objective control problem, hierarchical least-square optimization techniques are often employed, defining multiple tasks as objective functions, listed in hierarchical manner. The solution to the Inverse Kinematics problem requires to plan and constantly update the Cartesian trajectories. However, we propose an extension to the classical Hierarchical Quadratic Programming formulation, that allows to optimally generate these trajectories at control level.&lt;br&gt;
This is achieved by augmenting the optimization variable, to include the Cartesian reference and allow for the formulation of an adaptive compliance controller, which retains an impedance-like behaviour under external disturbances, while switching to an admittance-like behavior when collaborating with a human. The effectiveness of this approach is tested using a 7-DoF Franka Emika Panda manipulator in three different collaborative scenarios.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/871237/">871237</awardNumber>
      <awardTitle>Socio-physical Interaction Skills for Cooperative Human-Robot Systems in Agile Production</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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