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相关概念视频

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

490
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
490

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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用单个惯性传感器为双轴机器人运动接口的深度学习方法.

Tsige Tadesse Alemayoh1, Jae Hoon Lee1, Shingo Okamoto1

  • 1Department of Mechanical Engineering, Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama 790-8577, Ehime, Japan.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括

这项研究提出了使用深度学习和单个惯性测量单元 (IMU) 来控制双脚机器人的新框架. 它可以从最小的传感器数据或用户命令中进行机器人运动规划,实现精确的关节角度跟踪.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 机器学习 机器学习
  • 生物力学 生物力学

背景情况:

  • 双管式机器人运动控制通常需要复杂的传感器系统.
  • 人类移动捕获用于机器人控制可能昂贵且繁.
  • 在人机交互中,将人式运动整合到机器人中是一个关键的挑战.

研究的目的:

  • 为双脚机器人运动控制开发一个极简的框架.
  • 为了使单个惯性传感器数据或用户命令的机器人运动合成.
  • 为了实现精确的双脚机器人运动,模仿人类运动.

主要方法:

  • 在一个人身上使用单个惯性测量单元 (IMU) 来收集数据.
  • 采用Bi-LSTM编码器用于人类运动参数估计 (速度,步行阶段).
  • 开发了一种前运动发生器解码器网络,用于合成下肢关节角度.
  • 整合了富里埃数列方法,从用户命令 (速度,步行周期) 中生成运动参数.
  • 实现了对双脚机器人步行控制的约束一致的反向动力控制.
  • 使用MuJoCo物理引擎模拟验证了框架.

主要成果:

  • 该框架成功地从IMU数据和用户命令中合成了类似人类的运动参数.
关键词:
深度学习是一种深度学习.惯性传感器是一种无动态传感器.运动合成运动合成步行控制器 步行控制器

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  • 双脚机器人控制器实现了≤5°的联合角度跟踪误差.
  • 使用最小的传感器输入或简单的用户命令,证明了有效的机器人运动规划.

    • 结论:

    • 拟议的框架为双脚机器人运动控制提供了一种高效和具有成本效益的解决方案.
    • 最简单的传感和用户命令可以有效地驱动复杂的机器人运动.
    • 这项研究为先进的人机交互和控制系统提供了坚实的基础.