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Towards a seamless experimental protocol for human arm impedance estimation in an interactive dynamic task

机译:朝着交互式动态任务中的人臂阻抗估计的无缝实验方案

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The estimation of the human endpoint impedance interacting with a physical environment provides modelling insights both for the field of human movement science and for the design of innovative controllers for collaborative robotics based on physical human-robot interaction. Most of the existing human impedance estimation methods described in the literature rely on controlled environments closer to a laboratory than to an industrial setting. In this paper, a force perturbation method is proposed without any specific requirements on neither the force nor the position trajectories and without additional sensors placed on the human. The method is illustrated through an experimental study on a benchmark interaction task. The task was selected to be sufficiently variable for preventing the use of average trajectories and the perturbations used for the estimations are sufficiently low amplitude and short to not deteriorate the human performance in the task. The obtained impedance model parameters are in adequacy with the ones found in the literature, suggesting the validity of the proposed approach. The low constraints on the experimental settings make it applicable even out of the lab, for example, in human-robot collaboration in manufacturing environments, to adjust the robot behaviour to changes in the state of the operator (fatigue, stress) or to adjust to a variety of human operators with different interaction strategies.
机译:与物理环境相互作用的人类端点阻抗的估计为人体运动科学领域和基于物理人员机器人互动的协作机器人设计的创新控制器设计的建模洞察。文献中描述的大多数现有人的阻抗估计方法依赖于更接近实验室的受控环境而不是工业环境。在本文中,提出了一种力扰动方法,而没有任何特定要求的力,也不是位置轨迹,并且没有放置在人体上的额外传感器。该方法通过对基准交互任务的实验研究来说明。选择该任务是足够的变量,以防止使用平均轨迹,并且用于估计的扰动是足够低的幅度,短暂的不使任务中的人类性能降低。所获得的阻抗模型参数与文献中的内部发现的空间相当,表明提出的方法的有效性。实验设置上的低约束使得甚至在制造环境中的人机协作中适用于实验室,以调整机器人行为以改变操作员(疲劳,应力)或调整到各种具有不同互动策略的人类运营商。

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