分担负荷:使用肌肉活动的人机团队举重

Joseph DelPreto, D. Rus
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引用次数: 43

摘要

期望动作和目标的无缝沟通对于实现有效的人机协作至关重要。在这种情况下,通过表面肌电图(EMG)测量的肌肉活动可以洞察一个人的意图,同时最小限度地分散对任务的注意力。所提出的系统使用两个肌肉信号来创建一个控制框架,用于团队举起任务,其中人类和机器人一起举起物体。连续设定值算法使用二头肌活动来估计用户手高度的变化,并且还允许用户通过僵硬或放松手臂来明确调整机器人。除了这个管道之外,一个只对以前的用户进行训练的神经网络对二头肌和三头肌的活动进行分类,以检测滚动的上下手势;这样可以更好地控制机器人并扩展可行的工作空间。最终的系统由10名未经训练的受试者进行评估,这些受试者执行各种团队举起和装配刚性和柔性物体的任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sharing the Load: Human-Robot Team Lifting Using Muscle Activity
Seamless communication of desired motions and goals is essential for enabling effective physical human-robot collaboration. In such cases, muscle activity measured via surface electromyography (EMG) can provide insight into a person’s intentions while minimally distracting from the task. The presented system uses two muscle signals to create a control framework for team lifting tasks in which a human and robot lift an object together. A continuous setpoint algorithm uses biceps activity to estimate changes in the user’s hand height, and also allows the user to explicitly adjust the robot by stiffening or relaxing their arm. In addition to this pipeline, a neural network trained only on previous users classifies biceps and triceps activity to detect up or down gestures on a rolling basis; this enables finer control over the robot and expands the feasible workspace. The resulting system is evaluated by 10 untrained subjects performing a variety of team lifting and assembly tasks with rigid and flexible objects.
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