DEVISING A ROBOTIC ARM MANIPULANDUM FOR NORMAL AND ALTERED REACHING MOVEMENTS TO INVESTIGATE BRAIN MECHANISMS OF MOTOR CONTROL

The objective of this work was to develop a manipulandum system capable of measuring a monkey's arm movements and applying extrinsic forces to the hand. The manipulandum (RANARM) was designed as a five-bar linkage mechanism in order to realize sufficient rigidity and efficient force transmission. In addition, RANARM's control enables it to cancel its own dynamics by calculating the inverse dynamics. We evaluated the performance of RANARM from the mechanism, control, and application points of view. From the mechanism point of view, RANARM was able to provide a sufficiently large workspace for experiments, where it exhibited high manipulability and the capacity to apply force perturbations of at least 12 N under static conditions, and 5.3 N under dynamic conditions. From the control point of view, we estimated the uncompensated dynamics of RANARM to be negligibly small. Finally, we evaluated the performance of a monkey operating RANARM, and observed that the hand speed profiles followed a natural bell shape, while the maximal speeds were high (i.e., 15-cm reaching within 500 ms). In conclusion, RANARM allows a monkey to execute rapid movement and it is an efficient apparatus for investigating brain mechanisms for generating voluntary movements.

[1]  J. A. Pruszynski,et al.  Rapid motor responses are appropriately tuned to the metrics of a visuospatial task. , 2008, Journal of neurophysiology.

[2]  F. Freudenstein.  Advanced mechanism design: Analysis and synthesis: Vol. 2, by G. N. Sandor and A. G. Erdman. Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1984, 688 p , 1985 .

[3]  Xingyuan Wang,et al.  Nonlinear dynamic research on EEG signals in HAI experiment , 2009, Appl. Math. Comput..

[4]  P. Morasso.  Spatial control of arm movements , 2004, Experimental Brain Research.

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

[6]  E. J. Cheng,et al.  Morphometry of Macaca mulatta forelimb. I. Shoulder and elbow muscles and segment inertial parameters , 2000, Journal of morphology.

[7]  Stephen H Scott,et al.  Morphometry of macaca mulatta forelimb. III. moment arm of shoulder and elbow muscles , 2003, Journal of morphology.

[8]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[10]  Tsuneo Yoshikawa,et al.  Dynamic manipulability of robot manipulators , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[11]  宇野 洋二,et al.  Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model , 1988 .

[12]  M. Kawato,et al.  Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model , 2003 .

[13]  J. Cervantes-Sánchez,et al.  On the workspace, assembly configurations and singularity curves of the RRRRR-type planar manipulator , 2000 .

[14]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.

[15]  Charles A. Klein,et al.  Dexterity Measures for the Design and Control of Kinematically Redundant Manipulators , 1987 .

[16]  Mitsuo Kawato,et al.  Equilibrium-Point Control Hypothesis Examined by Measured Arm Stiffness During Multijoint Movement , 1996, Science.

[17]  Xingyuan Wang,et al.  Researches on chaos phenomenon of EEG dynamics model , 2006, Appl. Math. Comput..

[18]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[19]  J. Hollerbach,et al.  Time-varying stiffness of human elbow joint during cyclic voluntary movement , 2005, Experimental Brain Research.

[20]  J. A. Pruszynski,et al.  Long-Latency Reflexes of the Human Arm Reflect an Internal Model of Limb Dynamics , 2008, Current Biology.

[21]  J. Andrew Pruszynski,et al.  Primary motor cortex underlies multi-joint integration for fast feedback control , 2011, Nature.

[22]  M. Hallett,et al.  EMG analysis of stereotyped voluntary movements in man. , 1975, Journal of neurology, neurosurgery, and psychiatry.

[23]  Stephen H. Scott,et al.  Apparatus for measuring and perturbing shoulder and elbow joint positions and torques during reaching , 1999, Journal of Neuroscience Methods.

[24]  T. Oshima,et al.  Control properties induced by the existence of antagonistic pairs of bi-articular muscles-Mechanical engineering model analyses , 1994 .

[25]  Tsuneo Yoshikawa,et al.  Analysis and Control of Robot Manipulators with Redundancy , 1983 .

[26]  N.G. Hatsopoulos,et al.  Kinetic Trajectory Decoding Using Motor Cortical Ensembles , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Eizo Miyashita,et al.  A numerical simulation using optimal control can estimate stiffness profiles of a monkey arm during reaching movements , 2012, 2012 12th IEEE International Workshop on Advanced Motion Control (AMC).

[28]  Iven M. Y. Mareels,et al.  Stability and motor adaptation in human arm movements , 2005, Biological Cybernetics.

[29]  Daniel M. Wolpert,et al.  A modular planar robotic manipulandum with end-point torque control , 2009, Journal of Neuroscience Methods.

[30]  E. Bizzi,et al.  Human arm trajectory formation. , 1982, Brain : a journal of neurology.

[31]  D. B. Lockhart,et al.  Optimal sensorimotor transformations for balance , 2007, Nature Neuroscience.