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

Vincent Fortineau, M. Makarov, P. Rodríguez-Ayerbe, I. Siegler
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Abstract

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.
面向交互式动态任务中人体手臂阻抗估计的无缝实验方案
人体端点阻抗与物理环境相互作用的估计为人体运动科学领域和基于物理人机交互的协作机器人创新控制器的设计提供了建模见解。文献中描述的大多数现有人体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。本文提出了一种力摄动方法,该方法对力和位置轨迹都没有特定要求,并且不需要在人体上放置额外的传感器。通过对基准交互任务的实验研究说明了该方法的有效性。该任务被选择为足够可变以防止使用平均轨迹,用于估计的扰动幅度足够低且时间足够短,不会恶化人类在任务中的表现。得到的阻抗模型参数与文献中发现的参数是充分的,表明了所提出方法的有效性。对实验设置的低约束使其甚至适用于实验室之外,例如,在制造环境中的人机协作中,调整机器人行为以适应操作员状态(疲劳,压力)的变化,或适应具有不同交互策略的各种人类操作员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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