Buzzi Jacopo, Ferrigno Giancarlo, Jansma Joost M, De Momi Elena
Department of Electronics, Information and Bioengineering, Politecnico of Milan, Milan, Italy.
Mechanical Engineering Department, Delft University of Technology, >Delft, Netherlands.
Front Neurosci. 2017 Sep 26;11:528. doi: 10.3389/fnins.2017.00528. eCollection 2017.
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.
远程操作机器人系统正在多个不同领域广泛传播,从危险环境探索到手术。在远程操作中,用户直接操纵主设备以在从机器人端实现任务执行;这种交互对于保证系统稳定性和任务执行性能至关重要。在这项工作中,我们提出一种无干扰方法来研究手臂端点刚度。我们评估用户如何利用手臂的运动冗余在使用不同主设备执行不同任务期间实现稳定性和精度。四名用户被要求使用串联和并联连杆主设备按照虚拟任务执行两条平面轨迹。获取用户的手臂运动学和肌肉激活情况,并将其与特定于用户的肌肉骨骼模型相结合以估计关节刚度。利用手臂运动雅可比矩阵,推导出手臂端点刚度。所提出的无干扰方法能够在虚拟远程操作任务执行期间估计手臂端点刚度。所得结果与人类运动控制方面的现有文献一致,并且在整个测试轨迹中显示出受任务特征、手部速度和加速度影响的手臂端点刚度调制。