On the Value of Estimating Human Arm Stiffness during Virtual Teleoperation with Robotic Manipulators

Teleoperated robotic systems are widely spreading in multiple different fields, from hazardous environments exploration to surgery. In teleoperation, users directly manipulate a master device to achieve task execution at the slave robot side; this interaction is fundamental to guarantee both system stability and task execution performance. In this work, we propose a non-disruptive method to study the arm endpoint stiffness. We evaluate how users exploit the kinetic redundancy of the arm to achieve stability and precision during the execution of different tasks with different master devices. Four users were asked to perform two planar trajectories following virtual tasks using both a serial and a parallel link master device. Users' arm kinematics and muscular activation were acquired and combined with a user-specific musculoskeletal model to estimate the joint stiffness. Using the arm kinematic Jacobian, the arm end-point stiffness was derived. The proposed non-disruptive method is capable of estimating the arm endpoint stiffness during the execution of virtual teleoperated tasks. The obtained results are in accordance with the existing literature in human motor control and show, throughout the tested trajectory, a modulation of the arm endpoint stiffness that is affected by task characteristics and hand speed and acceleration.

[1]  D. Ostry,et al.  Learning to control arm stiffness under static conditions. , 2004, Journal of neurophysiology.

[2]  Giancarlo Ferrigno,et al.  Force feedback enhancement for soft tissue interaction tasks in cooperative robotic surgery , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Qi Shao,et al.  An EMG-driven model to estimate muscle forces and joint moments in stroke patients , 2009, Comput. Biol. Medicine.

[4]  Nikolaos G. Tsagarakis,et al.  A reduced-complexity description of arm endpoint stiffness with applications to teleimpedance control , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  G Somu,et al.  Robotic Telesurgery: Benefits Beyond Barriers , 2016 .

[6]  Michel Dhome,et al.  Hand-eye calibration , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[7]  Davide Piovesan,et al.  Experimental measure of arm stiffness during single reaching movements with a time-frequency analysis. , 2013, Journal of neurophysiology.

[8]  Nikolaos G. Tsagarakis,et al.  Tele-impedance: Teleoperation with impedance regulation using a body–machine interface , 2012, Int. J. Robotics Res..

[9]  Nima Enayati,et al.  Haptics in Robot-Assisted Surgery: Challenges and Benefits , 2016, IEEE Reviews in Biomedical Engineering.

[10]  E. Bizzi,et al.  The control of stable postures in the multijoint arm , 1996, Experimental Brain Research.

[11]  Huosheng Hu,et al.  Application of mobile agents to robust teleoperation of internet robots in nuclear decommissioning , 2003, IEEE International Conference on Industrial Technology, 2003.

[12]  Ron Daniel,et al.  Specification and design of input devices for teleoperation , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[13]  Dongjun Lee,et al.  Passive Bilateral Teleoperation With Constant Time Delay , 2006, IEEE Transactions on Robotics.

[14]  Toru Tsumugiwa,et al.  Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  Rieko Osu,et al.  The central nervous system stabilizes unstable dynamics by learning optimal impedance , 2001, Nature.

[16]  James Edward Colgate Coupled Stability of Multiport Systems—Theory and Experiments , 1994 .

[17]  Y. Koike,et al.  A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. , 2009, Journal of neurophysiology.

[18]  T. Flash,et al.  On the similarities between the perception and production of elliptical trajectories , 2006, Experimental Brain Research.

[19]  Costas S. Tzafestas,et al.  Visuo-Haptic Interface for Teleoperation of Mobile Robot Exploration Tasks , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[20]  Massimo Sartori,et al.  CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. , 2015, Journal of biomechanics.

[21]  Dario Farina,et al.  EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity , 2012, PloS one.

[22]  Anca Velisar,et al.  Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model , 2015, Computer methods in biomechanics and biomedical engineering.

[23]  S M McGill,et al.  The importance of normalization in the interpretation of surface electromyography: a proof of principle. , 1999, Journal of manipulative and physiological therapeutics.

[24]  Paul L Gribble,et al.  Role of cocontraction in arm movement accuracy. , 2003, Journal of neurophysiology.

[25]  Steven F. Wiker,et al.  Teleoperator comfort and psychometric stability: Criteria for limiting master-controller forces of operation and feedback during telemanipulation , 1989 .

[26]  Alireza Mirbagheri,et al.  A novel remote center of motion mechanism for the force‐reflective master robot of haptic tele‐surgery systems , 2014, The international journal of medical robotics + computer assisted surgery : MRCAS.

[27]  Giancarlo Ferrigno,et al.  Analysis of Joint and Hand Impedance During Teleoperation and Free-Hand Task Execution , 2017, IEEE Robotics and Automation Letters.

[28]  Rieko Osu,et al.  Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG. , 2002, Journal of neurophysiology.

[29]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.

[30]  D. Lloyd,et al.  An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. , 2003, Journal of biomechanics.

[31]  Dongjun Lee,et al.  Design and control of a low cost 6 DOF master controller , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[32]  Mitsuo Kawato,et al.  Human arm stiffness and equilibrium-point trajectory during multi-joint movement , 1997, Biological Cybernetics.

[33]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[34]  T. Flash,et al.  Human arm stiffness characteristics during the maintenance of posture , 1990, Experimental Brain Research.

[35]  Mahdi Tavakoli,et al.  Is the human operator in a teleoperation system passive? , 2013, 2013 World Haptics Conference (WHC).

[36]  E. B. Wilson,et al.  The Normal Logarithmic Transform , 1945 .

[37]  Oussama Khatib,et al.  Interface Design and Control Strategies for a Robot Assisted Ultrasonic Examination System , 2010, ISER.

[38]  Giancarlo Ferrigno,et al.  Adaptive Hands-On Control for Reaching and Targeting Tasks in Surgery , 2015 .

[39]  Patrick Helmer,et al.  A disturbance observer for the sigma.7 haptic device , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.