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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

390
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...
390
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

449
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...
449

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A Protocol for Real-time 3D Single Particle Tracking
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基于使用激光追踪器获得的位置和方向数据来识别机器人动力学参数的方法.

Jindong Wang1, Chenhao Yang1, Zhanyang Wu1

  • 1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.

The Review of scientific instruments
|October 7, 2024
PubMed
概括

本研究介绍了一种使用激光追踪器的机器人运动参数识别模型,以提高精度补偿. 它优化了激光追踪器的布局,并使用混合基因算法进行精确的校准.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计量学 计量学 计量学
  • 机械工程 机械工程

背景情况:

  • 准确的机器人运动参数识别对于有效的机器人精度补偿至关重要.
  • 现有的方法在识别动力参数错误时可能缺乏精度或效率.

研究的目的:

  • 开发一种快速而准确的方法来识别机器人运动参数错误.
  • 通过改进动力学参数识别来提高机器人的精度补偿.

主要方法:

  • 用于机器人姿势测量和动力参数识别的激光追踪器.
  • 分析了激光追踪器布局的影响,并开发了最佳定位的评估功能.
  • 构建了一个集成位置和定向数据的动态参数识别模型,通过混合基因算法与简单方法来增强,以实现最佳的校准姿势选择.

主要成果:

  • 建立了机器人端效应器偏差和动力参数错误之间的映射关系.
  • 证明综合位置和定位模型的准确性高于仅位置模型.
  • 实验结果验证了机器人运动参数识别的拟议模型和优化策略的有效性.

结论:

  • 开发的动力参数识别模型,利用激光追踪器测量和优化的校准策略,显著提高了机器人的精度.

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  • 混合遗传算法有效地解决了过早的融合问题,导致了更强大的识别过程.
  • 这种方法为精确的机器人校准和增强性能提供了可靠的解决方案.