Transferring Human Impedance Regulation Skills to Robots

180 Pages·2016·6.555 MB·English

by  Arash Ajoudani (auth.)

Springer Tracts in Advanced Robotics 110
Arash Ajoudani
Transferring
Human
Impedance
Regulation Skills
to Robots
Springer Tracts in Advanced Robotics 110
Editors
Prof. Bruno Siciliano Prof. Oussama Khatib
Dipartimento di Ingegneria Elettrica Artificial Intelligence Laboratory
e Tecnologie dell’Informazione Department of Computer Science
Università degli Studi di Napoli Stanford University
Federico II Stanford, CA 94305-9010
Via Claudio 21, 80125 Napoli USA
Italy E-mail: [email protected]
E-mail: [email protected]
Editorial Advisory Board
Oliver Brock, TU Berlin, Germany
Herman Bruyninckx, KU Leuven, Belgium
Raja Chatila, ISIR—UPMC & CNRS, France
Henrik Christensen, Georgia Tech, USA
Peter Corke, Queensland Univ. Technology, Australia
Paolo Dario, Scuola S. Anna Pisa, Italy
Rüdiger Dillmann, University of Karlsruhe, Germany
Ken Goldberg, UC Berkeley, USA
John Hollerbach, University of Utah, USA
Makoto Kaneko, Osaka University, Japan
Lydia Kavraki, Rice University, USA
Vijay Kumar, University of Pennsylvania, USA
Sukhan Lee, Sungkyunkwan University, Korea
Frank Park, Seoul National University, Korea
Tim Salcudean, University of British Columbia, Canada
Roland Siegwart, ETH Zurich, Switzerland
Gaurav Sukhatme, University of Southern California, USA
Sebastian Thrun, Stanford University, USA
Yangsheng Xu, The Chinese University of Hong Kong, PRC
Shin’ichi Yuta, Tsukuba University, Japan
More information about this series at http://www.springer.com/series/5208
STAR (Springer Tracts in Advanced Robotics) has been promoted
undertheauspicesofEURON(EuropeanRoboticsResearchNetwork)
Arash Ajoudani
Transferring Human
Impedance Regulation Skills
to Robots
123
Arash Ajoudani
Department ofAdvanced Robotics
Italian Institute of Technology (IIT)
Genova
Italy
ISSN 1610-7438 ISSN 1610-742X (electronic)
SpringerTracts inAdvanced Robotics
ISBN978-3-319-24203-3 ISBN978-3-319-24205-7 (eBook)
DOI 10.1007/978-3-319-24205-7
LibraryofCongressControlNumber:2015950046
SpringerChamHeidelbergNewYorkDordrechtLondon
©SpringerInternationalPublishingSwitzerland2016
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Foreword
Robotics is undergoing a major transformation in scope and dimension. From a
largely dominant industrial focus, robotics is rapidly expanding into human envi-
ronmentsandvigorouslyengagedinitsnewchallenges. Interactingwith,assisting,
serving, and exploring with humans, the emerging robots will increasingly touch
people and their lives.
Beyond its impact on physical robots, the body of knowledge robotics has
produced is revealing a much wider range of applications reaching across diverse
research areas and scientific disciplines, such as: biomechanics, haptics, neurosci-
ences,virtualsimulation,animation,surgery,andsensornetworksamongothers.In
return,thechallengesofthenewemergingareasareprovinganabundantsourceof
stimulation and insights for the field of robotics. It is indeed at the intersection of
disciplines that the most striking advances happen.
TheSpringer TractsinAdvancedRobotics(STAR)isdevotedtobringingtothe
research community the latest advances in the robotics field on the basis of their
significance and quality. Through a wide and timely dissemination of critical
researchdevelopmentsinrobotics,ourobjectivewiththisseriesistopromotemore
exchanges and collaborations among the researchers in the community and con-
tribute to further advancements in this rapidly growing field.
The monograph by Arash Ajoudani is a contribution aimed at developing
teleimpedancecontroltechniquesforroboticsystems,namelyamanipulatorarm,a
dual-arm setup, a synergy-driven robotic hand, and a compliant exoskeleton. The
concepts of common mode stiffness and configuration dependent stiffness control
areintroducedandexperimentallyevaluatedinanassemblytask.Anovelreal-time
motioncontrolstrategyisproposedtoregulatethedesiredCartesianstiffnessprofile
during the execution of observed bimanual tasks. Tactile feedback is used to
develop intuitive control schemes for grasping tasks, while EMG signals are used
for estimating knee joint torques, trajectory, and stiffness in real time. The results
described in the volume suggest that the incorporation of human motor control
principles into the design of robot controllers is expected to lead to versatile and
stable behaviors when interacting with environments with dynamic uncertainties.
v
vi Foreword
Remarkably,themonographisbasedontheauthor’sdoctoralthesis,whichwas
afinalistforthe2015GeorgesGiralt Ph.D.Award.AveryfineadditiontoSTAR!
Naples, Italy Bruno Siciliano
July 2015 STAR Editor
Acknowledgments
First of all, I would like to thank Prof. Antonio Bicchi, Dr. Nikos Tsagarakis and
Prof. Darwin Caldwell for their outstanding support and valuable guidelines
throughout my doctoral studies. Without their encouragement, constructive criti-
cism, and constant feedback, my Ph.D. would not have been achievable. I would
alsoliketothankProf.MarcoGabicciniwhoseadviceandinsightswereinvaluable
to me.
Robotics is a particular research domain which demands for collaborative
interactionsand keen enthusiasm. Thus, it isextremely importantto work within a
team of innovative and enthusiastic people. Advanced Robotics Department of
Italian Institute of Technology, together with Centro E. Piaggio Department of
University of Pisa, as international and dynamic working environments, provided
me with the possibility of meeting and working with many brilliant researchers
whose collaboration was extremely productive and led to a number of studies and
several publications. Among the many colleagues those whose support and
encouragement have been rewarding to me are: Dr. Jinoh Lee, Dr. Sasha Blue
Godfery, Dr. Matteo Bianchi, Dr. Manuel Catalano, and Dr. Giorgio Grioli.
I amthankfulto my mother, Fereshteh, for her love and support throughout the
years, through good times and bad. I am grateful to my father, Javad, who always
gave me wise and insightful counsel, and I will always keep his memories in my
heart.Iwouldliketothankmybother,Nima,forbeingsupportiveandmydearest,
Elena, for her love.
Finally,Iwouldliketothankforthegenerousfundingofmywork,providedby
the European projects: The Hand Embodied [31], Softhands [32], SAPHARI [33],
WEARHAPS [34], and WALKMAN [35].
Genova Arash Ajoudani
July 2015
vii
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Outline of Part I. . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.2 Outline of Part II . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.3 Outline of Part III. . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.4 Outline of Part IV. . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 On the Role of Compliance and Geometry in Mechanical
Stability of the Humans and Robots . . . . . . . . . . . . . . . . . . . . . . 9
2.1 Stability in Human-Environment Interactions . . . . . . . . . . . . . 9
2.2 Compliant Behavior in Robots . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Compliant Mechanisms . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Compliance Control . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Redundancy Resolution and Its Application
to Impedance Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Part I Teleimpedance Control of a Robotic Arm
3 Teleimpedance: Teleoperation with Impedance
Regulation Using a Body-Machine Interface. . . . . . . . . . . . . . . . . 19
3.1 Human Arm Impedance Modeling in 3D . . . . . . . . . . . . . . . 22
3.2 Stiffness Model Calibration/Identification. . . . . . . . . . . . . . . . 25
3.2.1 Identification of the EMG-to-Force Map . . . . . . . . . . 27
3.2.2 Identification of the EMG-to-Stiffness Map . . . . . . . . 27
3.2.3 Identification Results. . . . . . . . . . . . . . . . . . . . . . . . 30
ix
x Contents
4 Replicating Human Stiffness Profile with a Cartesian
Impedance Controller in Realtime. . . . . . . . . . . . . . . . . . . . . . . . 33
4.1 Cartesian Impedance Control . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Teleimpedance: Peg-in-Hole Task. . . . . . . . . . . . . . . . . . . . . 35
4.2.1 Experimental Results. . . . . . . . . . . . . . . . . . . . . . . . 37
4.3 Teleimpedance: Ball-Catching Task. . . . . . . . . . . . . . . . . . . . 40
4.3.1 Experimental Results. . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 Exploring the Roles of Common Mode Stiffness (CMS)
and Configuration Dependent Stiffness (CDS) Control . . . . . . . . . 47
5.1 Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.1.1 CMS-CDS Controller . . . . . . . . . . . . . . . . . . . . . . . 48
5.1.2 Minimum-Effort Controller . . . . . . . . . . . . . . . . . . . 52
5.1.3 Soft Switching Logic. . . . . . . . . . . . . . . . . . . . . . . . 52
5.2 Experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.3 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Part II Human-like Impedance Control of a Dual-Arm Manipulator
6 Natural Redundancy Resolution in Dual-Arm Manipulation
Using CDS Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.1 Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.1.1 Dual-Arm Kinematics . . . . . . . . . . . . . . . . . . . . . . . 63
6.1.2 Impedance Control of Dual-Arm. . . . . . . . . . . . . . . . 64
6.1.3 Task Prioritization. . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.2 Experimental Setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.4 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Part III Teleimpedance Control of a Robotic Hand
7 A Synergy-Driven Approach to a Myoelectric Hand. . . . . . . . . . . 77
7.1 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.1.1 Overall Study Design . . . . . . . . . . . . . . . . . . . . . . . 78
7.1.2 The Pisa/IIT SoftHand. . . . . . . . . . . . . . . . . . . . . . . 79
7.1.3 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.1.4 EMG Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.1.5 Control Architecture . . . . . . . . . . . . . . . . . . . . . . . . 83
7.1.6 Questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7.1.7 Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.3 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88