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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

675
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
675
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

854
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
854

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相关实验视频

Updated: Jan 8, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

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Published on: August 29, 2025

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实时人类进步估计与在线动态时间扭曲协作机器人的机器人技术.

Davide De Lazzari1, Matteo Terreran1, Giulio Giacomuzzo1

  • 1Department of Information Engineering, University of Padua, Padua, Italy.

Frontiers in robotics and AI
|December 22, 2025
PubMed
概括

本研究引入了新的动态时间扭曲 (DTW) 方法,用于实时人类行动进度估计,增强人机协作. 该PACE框架提高了交互流性,并减少了用户研究中的等待时间.

关键词:
协作组装 协作组装是一种协作组装.人类行动完成时间预测时间预测人类行动进度估计人与机器人的交互开放式的动态时间扭曲.实时监控实时监控强化学习是一种强化学习.滑窗交叉相关性 滑窗交叉相关性

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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
  • 机器学习 机器学习

背景情况:

  • 实时的人类行动进度估计对于有效的人机协作至关重要.
  • 目前用于行动进展估计的方法未得到充分探索,限制了无协作.

研究的目的:

  • 提出开放式软DTW (OS-DTWEU) 的第一个实时应用.
  • 引入OS-DTWWP,这是一个新的DTW变体,用于捕获本地相关性.
  • 通过行动完成估计 (PACE) 制定主动协助框架,用于同步的机器人协助.

主要方法:

  • 实现了实时的OS-DTWEU,并引入了具有Windowed-Pearson距离的OS-DTWWP.
  • 将这些方法集成到PACE框架中,使用强化学习来估计行动的完成.
  • 对12名参与者进行了椅子组装任务和用户研究的实验.

主要成果:

  • OS-DTWWP在捕捉本地运动模式方面表现出优越性.
  • OS-DTWEU在具有一致绝对立场的任务中被证明是有效的.
  • 该PACE框架显著改善了交互流性,减少了等待时间,并获得了积极的用户反.

结论:

  • 拟议的OS-DTW变体和PACE框架在实时人类行动进度估计方面取得了重大进展.
  • 这项研究使人机协作更加流和高效.
  • 这些发现突出了适应性机器人协助复杂任务的潜力.