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相关概念视频

Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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相关实验视频

Updated: Jun 18, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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使用HMM的轨迹学习:朝着手术机器人实施的方向

Juliana Manrique-Cordoba1, Carlos Martorell-Llobregat2, Miguel Ángel de la Casa-Lillo1

  • 1Bioengineering Institute, Miguel Hernandez University of Elche, 03202 Elche, Spain.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过使用从演示学习 (LfD) 提高了手术机器人的自主性. 将力数据纳入轨迹学习可以显著提高机器人手术应用的准确性.

关键词:
隐藏的马尔科夫模型从演示中学习.机器人学习轨迹的学习轨迹手术机器人手术机器人手术机器人简化了轨迹的简化.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 手术技术 手术技术
  • 机器学习 机器学习

背景情况:

  • 手术机器人自主性正在迅速发展.
  • 从演示中学习 (LfD) 对于开发自主手术系统至关重要.
  • 目前的LfD方法需要增强的轨迹表示.

研究的目的:

  • 为了改善外科手术机器人在高维空间中的轨迹概括.
  • 使用多维数据开发一个更全面的证明轨迹的表现.
  • 增强手术应用中LfD信息的编码和解释.

主要方法:

  • 扩展了Douglas-Peucker算法,包括动力学和动态轨迹数据.
  • 收集并预处理的运动和力相互作用数据.
  • 训练了一个隐藏的马尔科夫模型 (HMM),比较仅运动与运动与力数据.

主要成果:

  • 包括力相互作用数据改善了轨迹重建的准确性.
  • 使用力数据实现了0.29mm的下根平均平方误差 (RMSE),而没有力数据则为0.44mm.
  • 在高维空间中展示了增强的概括性.

结论:

  • 提出的方法有效地编码,简化和学习机器人轨迹.
  • 整合多维轨迹数据,包括力,对手术机器人中的LfD有益.
  • 这种方法促进了自主手术系统的发展.