A modular planar robotic manipulandum with end-point torque control.
Identifieur interne : 004385 ( Main/Merge ); précédent : 004384; suivant : 004386A modular planar robotic manipulandum with end-point torque control.
Auteurs : Ian S. Howard [Royaume-Uni] ; James N. Ingram ; Daniel M. WolpertSource :
- Journal of neuroscience methods [ 1872-678X ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Robotics.
- physiology : Muscle, Skeletal.
- physiopathology : Muscle Rigidity.
- Arm, Biophysical Processes, Equipment Design, Humans, Learning, Motor Skills, Movement, Range of Motion, Articular, Torque.
Abstract
Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.
DOI: 10.1016/j.jneumeth.2009.05.005
PubMed: 19450621
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pubmed:19450621Le document en format XML
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Howard</name><affiliation wicri:level="4"><nlm:affiliation>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB21PZ, UK. ish22@cam.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB21PZ</wicri:regionArea><orgName type="university">Université de Cambridge</orgName><placeName><settlement type="city">Cambridge</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Angleterre de l'Est</region></placeName></affiliation></author><author><name sortKey="Ingram, James N" sort="Ingram, James N" uniqKey="Ingram J" first="James N" last="Ingram">James N. Ingram</name></author><author><name sortKey="Wolpert, Daniel M" sort="Wolpert, Daniel M" uniqKey="Wolpert D" first="Daniel M" last="Wolpert">Daniel M. Wolpert</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="doi">10.1016/j.jneumeth.2009.05.005</idno><idno type="RBID">pubmed:19450621</idno><idno type="pmid">19450621</idno><idno type="wicri:Area/PubMed/Corpus">001275</idno><idno type="wicri:Area/PubMed/Curation">001275</idno><idno type="wicri:Area/PubMed/Checkpoint">001169</idno><idno type="wicri:Area/Ncbi/Merge">001147</idno><idno type="wicri:Area/Ncbi/Curation">001147</idno><idno type="wicri:Area/Ncbi/Checkpoint">001147</idno><idno type="wicri:Area/Main/Merge">004385</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A modular planar robotic manipulandum with end-point torque control.</title><author><name sortKey="Howard, Ian S" sort="Howard, Ian S" uniqKey="Howard I" first="Ian S" last="Howard">Ian S. Howard</name><affiliation wicri:level="4"><nlm:affiliation>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB21PZ, UK. ish22@cam.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB21PZ</wicri:regionArea><orgName type="university">Université de Cambridge</orgName><placeName><settlement type="city">Cambridge</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Angleterre de l'Est</region></placeName></affiliation></author><author><name sortKey="Ingram, James N" sort="Ingram, James N" uniqKey="Ingram J" first="James N" last="Ingram">James N. Ingram</name></author><author><name sortKey="Wolpert, Daniel M" sort="Wolpert, Daniel M" uniqKey="Wolpert D" first="Daniel M" last="Wolpert">Daniel M. Wolpert</name></author></analytic><series><title level="j">Journal of neuroscience methods</title><idno type="eISSN">1872-678X</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Arm</term><term>Biophysical Processes</term><term>Equipment Design</term><term>Humans</term><term>Learning</term><term>Motor Skills</term><term>Movement</term><term>Muscle Rigidity (physiopathology)</term><term>Muscle, Skeletal (physiology)</term><term>Range of Motion, Articular</term><term>Robotics (instrumentation)</term><term>Torque</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Robotics</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Muscle, Skeletal</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Muscle Rigidity</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Arm</term><term>Biophysical Processes</term><term>Equipment Design</term><term>Humans</term><term>Learning</term><term>Motor Skills</term><term>Movement</term><term>Range of Motion, Articular</term><term>Torque</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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