Task Location for High Performance Human-Robot Collaboration

In this paper, an approach for the evaluation of human-robot collaboration towards high performance is introduced and implemented. The human arm and the manipulator are modelled as a closed kinematic chain and the proposed task performance criterion is used based on the manipulability index of this chain. The selected task is a straight motion in which the robot end-effector is guided by the human operator via an admittance controller. The best location of the selected task is determined by the maximization of the minimal manipulability along the path. Evaluation criteria for the performance are adopted considering the ergonomics literature. In the experimental set-up with a KUKA LWR manipulator, multiple subjects repeat the specified motion to evaluate the introduced approach experimentally.

[1]  Keith L. Doty,et al.  NONCOMMENSURATE SYSTEMS IN ROBOTICS , 2014 .

[2]  Arnold J.W.M. Thomassen,et al.  Effects of handedness and arm position on stroke-direction preferences in drawing , 1992 .

[3]  Natalia Dounskaia,et al.  A preferred pattern of joint coordination during arm movements with redundant degrees of freedom. , 2014, Journal of neurophysiology.

[4]  F. Grund Forsythe, G. E. / Malcolm, M. A. / Moler, C. B., Computer Methods for Mathematical Computations. Englewood Cliffs, New Jersey 07632. Prentice Hall, Inc., 1977. XI, 259 S , 1979 .

[5]  Jadran Lenarčič,et al.  Advances in Robot Kinematics: Theory and Applications , 2013 .

[6]  Michael A. Malcolm,et al.  Computer methods for mathematical computations , 1977 .

[7]  Philippe Gorce,et al.  The manipulability: a new index for quantifying movement capacities of upper extremity , 2012, Ergonomics.

[8]  Ruud G. J. Meulenbroek,et al.  Stroke-direction preferences in drawing and handwriting , 1991 .

[9]  F. Jean,et al.  Why Don't We Move Slower? The Value of Time in the Neural Control of Action , 2016, The Journal of Neuroscience.

[10]  Yoshiyuki Tanaka,et al.  Manipulability analysis of human arm movements during the operation of a variable-impedance controlled robot , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  C. Gosselin The optimum design of robotic manipulators using dexterity indices , 1992, Robotics Auton. Syst..

[12]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  Jorge Angeles,et al.  On the kinematic conditioning of robotic manipulators , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[14]  Stephan P. Swinnen,et al.  Directional tuning effects during cyclical two-joint arm movements in the horizontal plane , 2001, Experimental Brain Research.

[15]  Mark S. Sanders,et al.  Human Factors in Engineering and Design , 1957 .

[16]  John T. Wen,et al.  Kinematic manipulability of general constrained rigid multibody systems , 1999, IEEE Trans. Robotics Autom..

[17]  Natalia Dounskaia,et al.  Strategy of arm movement control is determined by minimization of neural effort for joint coordination , 2016, Experimental Brain Research.

[18]  J. Kenneth Salisbury,et al.  Articulated Hands , 1982 .

[19]  Lionel Rigoux,et al.  A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control , 2012, PLoS Comput. Biol..

[20]  Pyung H. Chang A Dexterity Measure for the Kinematic Control of Robot Manipulator with Redundancy , 1988 .

[21]  Steven C. Seow Information Theoretic Models of HCI: A Comparison of the Hick-Hyman Law and Fitts' Law , 2005, Hum. Comput. Interact..

[22]  R. Shadmehr,et al.  Temporal Discounting of Reward and the Cost of Time in Motor Control , 2010, The Journal of Neuroscience.

[23]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[24]  Sarosh H. Patel,et al.  Manipulator Performance Measures - A Comprehensive Literature Survey , 2015, J. Intell. Robotic Syst..

[25]  E. Burdet,et al.  The duration of reaching movement is longer than predicted by minimum variance. , 2016, Journal of neurophysiology.

[26]  Antonio Bicchi,et al.  Manipulability of cooperating robots with unactuated joints and closed-chain mechanisms , 2000, IEEE Trans. Robotics Autom..

[27]  Tsuneo Yoshikawa,et al.  Manipulability of Robotic Mechanisms , 1985 .

[28]  Karl M Newell,et al.  The movement speed-accuracy relation in space-time. , 2013, Human movement science.

[29]  Natalia V Dounskaia,et al.  Directional biases reveal utilization of arm's biomechanical properties for optimization of motor behavior. , 2007, Journal of neurophysiology.

[30]  Arash Ajoudani,et al.  Towards ergonomie control of human-robot co-manipulation and handover , 2017, 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids).

[31]  W Karwowski,et al.  The boundaries for joint angles of isocomfort for sitting and standing males based on perceived comfort of static joint postures , 2001, Ergonomics.

[32]  Nikos A. Aspragathos,et al.  Variable Admittance Control for Human-Robot Collaboration based on Online Neural Network Training , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[33]  Pradeep K. Khosla,et al.  Dexterity measures for design and control of manipulators , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[34]  Cyril F. Reboul,et al.  Epitope Flexibility and Dynamic Footprint Revealed by Molecular Dynamics of a pMHC-TCR Complex , 2012, PLoS Comput. Biol..

[35]  N. Dounskaia,et al.  The role of vision, speed, and attention in overcoming directional biases during arm movements , 2011, Experimental Brain Research.

[36]  Mark S. Sanders,et al.  Human factors in engineering and design, 7th ed. , 1993 .