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相关概念视频

Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

791
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
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791
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

879
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...
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相关实验视频

Updated: Feb 28, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

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Published on: October 1, 2019

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基于实时形状估计的连续机器人的混合离线-在线配置规划方法.

Hexiang Yuan1,2, Zhibo Jing1,2,3, Yibo He1,2

  • 1College of Artificial Intelligence, Nankai University, Tianjin 300350, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括

这项研究介绍了连续机器人的混合规划框架,提高了它们在复杂空间安全导航的能力. 这种方法可以在狭窄的环境中提高运动的流性和控制.

关键词:
避免碰撞,避免碰撞.配置规划 配置规划配置精细化改进 配置精细化改进连续机器人机器人连续机器人形状估计,形状估计.

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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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相关实验视频

Last Updated: Feb 28, 2026

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统 控制系统
  • 机械工程 机械工程

背景情况:

  • 连续机器人为狭窄空间提供高灵活性.
  • 它们固有的非线性和不确定性给运动规划带来了挑战.

研究的目的:

  • 开发一种混合线下线上框架,用于连续机器人运动规划.
  • 在复杂的环境中解决避免碰撞和结构约束的问题.

主要方法:

  • 制定规划作为与碰撞和结构约束的非线性优化.
  • 使用与差异进化 (DE) 的共同进化策略来优化分解.
  • 实现了一个无气味的卡尔曼波器 (UKF) 实时形状估计和安全距离监控.
  • 设计了一个在线配置精炼器,根据形状反进行本地调整.

主要成果:

  • 证明了混合规划框架的有效性.
  • 在现实世界的测试中实现了增强的运动流性和安全的运动性能.
  • 通过受限空间成功引导连续操纵器.

结论:

  • 拟议的混合框架有效地管理连续机器人的非线性.
  • 这种方法提高了在充满挑战的环境中安全和顺的航行.
  • 该方法为连续机器人运动规划提供了强大的解决方案.