一种基于神经机械模型的策略,用于估计操作员在工业升降任务中的有效载荷
概括
这项研究引入了一种新的工业外骨方法,可以使用个性化的电肌图驱动肌肉骨模型 (pEMS) 和三角形扭矩方法准确估计有效载荷. 这种技术提高了辅助准确性,并且需要更少的训练数据以更好地泛化.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 生物力学 生物力学
背景情况:
- 活跃的工业外骨需要准确的有效载荷估计,以便有效地协助用户.
- 当前的有效载荷估计方法往往缺乏准确性,需要广泛的校准,或严重依赖机器学习与大数据集.
- 估计有效载荷对于外骨来说至关重要,以动态适应并在任务中提供最佳支持.
研究的目的:
- 为活跃的工业外骨架开发和验证一种新的有效载荷估计方法.
- 为了提高外骨有效载荷估计算法的准确性和缩短校准时间.
- 证明拟议方法在各种有效负载和任务中的概括能力.
主要方法:
- 概念化了一种有效载荷估计方法,将个性化的肌肉骨模型 (pEMS) 与"三角形扭矩"方法相结合.
- 德尔塔扭矩方法有效地将有效载荷动态与人力动态分开.
- 该方法通过对人类操作员执行工业升降任务的实验得到了验证.
主要成果:
- 拟议的方法在验证的举重任务 (0kg,5kg,10kg,15kg) 中实现了平均绝对平均误差 (MAE) 约1.4公斤.
- 在训练组之外的任务 (5公斤和10公斤) 显示低MAE分别为1.6公斤和1.1公斤,表明强烈的泛化.
- 与现有的基于机器学习的解决方案相比,该方法表现出更高的性能,需要更小的训练数据集.
结论:
- 开发的基于pEMS的有效载荷估计方法为活跃的工业外骨提供了重大进展.
- 这种基于模型的方法提供了准确和可概括的有效载荷估计,克服了当前技术的局限性.
- 这代表了第一个已知的EMG驱动型基于模型的方法在外骨中估计人类有效载荷的应用.
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