Jove
Visualize
联系我们

相关概念视频

Root-Locus Method01:19

Root-Locus Method

623
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
623

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Novel Approach to Speed Up Hampel Filter for Outlier Detection.

Sensors (Basel, Switzerland)·2025
Same author

Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise.

Sensors (Basel, Switzerland)·2023
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: May 5, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K

强化学习方法用于优化表面检查的度传感器轨迹.

Sara Roos-Hoefgeest1, Mario Roos-Hoefgeest2, Ignacio Álvarez1

  • 1Department of Electrical, Computer Electronics and Systems Engineering, University of Oviedo, 33003 Oviedo, Spain.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括

这项研究引入了一种新的强化学习 (RL) 方法,以优化激光三角测定型度传感器的表面检查轨迹. 该方法通过动态调整传感器运动以实现一致的高质量扫描来提高缺陷检测的准确性.

关键词:
没有NDT的NDT.自动光学检查自动光学检查工业机器人 工业机器人 工业机器人激光辐射激光辐射的辐射.强化学习是一种强化学习.表面特征 表面特征轨道规划 轨道规划 轨道规划

更多相关视频

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.4K
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

13.9K

相关实验视频

Last Updated: May 5, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.4K
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

13.9K

科学领域:

  • 制造业 制造技术 制造技术
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 高精度的表面缺陷检测在制造中至关重要.
  • 激光三角测定型度传感器提供详细的表面测量.
  • 精确的机器人运动对于最佳的传感器性能至关重要.

研究的目的:

  • 开发一种新的强化学习 (RL) 方法来优化检查轨迹.
  • 为了提高表面缺陷检测,使用形测量传感器.
  • 通过动态轨迹调整来确保一致的配置分布和高质量的扫描.

主要方法:

  • 使用了强化学习 (RL) 模型,特别是近接政策优化 (PPO) 算法.
  • 设计了一个量身定制的状态空间,动作空间和奖励功能,用于配置测量传感器检查.
  • 采用模拟环境与现实的条件 (传感器噪音,表面不规则) 在线轨迹规划使用CAD模型.

主要成果:

  • RL代理成功优化了配置测量传感器的检查轨迹.
  • 该方法在确保一致的配置分布和高质量的扫描方面表现出有效性.
  • 用UR3e机器人臂在模拟和现实世界测试中的验证证实了该模型的实用性.

结论:

  • 提出的基于RL的方法为优化表面检查轨迹提供了有效的解决方案.
  • 这种方法显著提高了在制造过程中检测缺陷的精度和效率.
  • 动态轨迹优化可提高激光三角化特征测量传感器在现实应用中的性能.