辅助机器人控制的进化,以满足最终用户的能力
Andrew Thompson1, Fabio Rizzoglio1, Fiona A Neylon1
1Northwestern University, Shirley Ryan Ability Lab, Chicago, IL, USA.
概括
这项研究开发了一种控制系统,将患者的残留身体运动转化为机器人手臂控制. 这一进步增强了对上肢残疾用户的辅助机器人手臂操作.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 神经科学是一个神经科学.
- 康复工程 康复工程
背景情况:
- 辅助机器人手臂为上肢患者提供了潜在的帮助.
- 操作高自由度 (DoF) 机器人手臂对这一群体构成重大控制挑战.
- 现有的控制方法可能无法充分将残余电机控制转化为直观的机器人操作.
研究的目的:
- 开发和评估用于使用剩余身体运动远程操作7-DOF辅助机器人手臂的控制图.
- 为了将神经运动障碍的个体的低变量身体运动转换为6D速度控制信号.
- 通过实验研究分析控制图的有效性.
主要方法:
- 设计和完善一个控制系统,将剩余的身体运动转化为机器人控制信号.
- 使用惯性测量单元 (IMU) 数据来捕捉身体运动.
- 对来自受损和不受损人群的IMU信号进行差异分析.
- 分析控制图数据集的内在维度,有或没有运动指导.
- 进行13个会议的初步研究,以验证开发的控制地图.
主要成果:
- 展示了一种方法,将低方差的残余身体运动转换为6D速度控制信号.
- 在神经运动受损和未受损的个体之间确定了IMU信号变异的差异.
- 描述了运动指导对控制图数据集维度的影响.
- 初步研究结果表明,开发的控制图对于辅助机器人手臂操作的可行性.
结论:
- 开发的控制地图显示了使上肢的个体能够操作高DoF辅助机器人手臂的前景.
- 了解信号差异和数据集维度对于设计有效的控制系统至关重要.
- 需要进一步的研究和验证,以优化该系统,以便广泛的临床使用.
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