Model Of Human Arm Research Articles

1118 人の腕の運動モデルを応用したフレキシブルアームの制御(ロボティクス・メカトロニクスVIII)

1118 人の腕の運動モデルを応用したフレキシブルアームの制御(ロボティクス・メカトロニクスVIII)

Human Arm Trajectory: Planning, Execution, and Control

A three dimensional, five degrees of freedom human arm model has been developed to study the planning, execution and control of hand movement. The hand trajectory is assumed to derive from the population vector of neural cells of motor cortex. The real time calculation of joint trajectories from the hand trajectory is based on a biologically inspired algorithm. The instantaneous angular velocity for each joint is determined by the cross product of two vectors: the joint vector and the error vector. A nonlinear gain modulation adjusts the gain for each degree of freedom according to the instantaneous joint position relative to its physiological limit. The algorithm does not use inverse kinematics to determine joint trajectories from the hand position, and can be adapted to situations where the final target position is moving, for example, to track or to intercept a moving object. Several simulation results are presented to illustrate the merit of the algorithm.

Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse models

We predict the virtual trajectories and stiffness ellipses during multijoint arm movements by computer simulations. A two-link manipulator with four single-joint muscles and two double-joint muscles is used as a model of the human arm. Physical parameters of the model are derived from several experimental data. Among them, special emphasis is put on low values of the dynamic hand stiffness recently measured during single-joint and multijoint movements. The feedback-error-learning scheme to acquire the inverse dynamics model and the inverse statics model is utilized for this prediction. The virtual trajectories are much more complex than the actual trajectories. This indicates that planning the virtual trajectory is as difficult as solving the inverse dynamics problem for medium and fast movements, and simply falsifies the advocated computational advantage of the virtual trajectory control hypothesis. Thus, we conclude that learning inverse models is essential even in the virtual trajectory control framework. Finally, we propose a new computational model to learn the complicated shape of the virtual trajectories by integrating the virtual trajectory control and the feedback-error-learning scheme.