Jove
Visualize
联系我们

相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

379
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...
379
Instantaneous Center of Zero Velocity01:20

Instantaneous Center of Zero Velocity

430
General plane motion, often observed in a rolling wheel, refers to a type of movement where the wheel is simultaneously rotating and translating. This complex motion can be understood by breaking it down into individual components.
To analyze this, consider two points on the wheel: point A and point B. The absolute velocity of point B can be expressed as the vector sum of the absolute velocity of point A and the relative velocity of point B with respect to point A. To simplify this analysis,...
430
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

439
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...
439
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

275
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
275
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

316
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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
316
Gyroscope: Precession01:24

Gyroscope: Precession

3.9K
Precession can be demonstrated effectively through a spinning top. If a spinning top is placed on a flat surface near the surface of the Earth at a vertical angle and is not spinning, it will fall over due to the force of gravity producing a torque acting on its center of mass. However, if the top is spinning on its axis, it precesses about the vertical direction, rather than topple over due to this torque. Precessional motion is a combination of a steady circular motion of the axis and the...
3.9K

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

Design of a 3D High-Definition Map Visualizer for Pose Estimation and Autonomous Navigation in Dynamic Environments.

Sensors (Basel, Switzerland)·2026
Same author

Game-Theoretic Motion Planning with Perception Uncertainty and Right-of-Way Constraints.

Sensors (Basel, Switzerland)·2025
Same author

Consensus-Based Information Filtering in Distributed LiDAR Sensor Network for Tracking Mobile Robots.

Sensors (Basel, Switzerland)·2024
Same author

Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation.

Sensors (Basel, Switzerland)·2023
Same author

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion.

Sensors (Basel, Switzerland)·2018
Same author

Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
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 21, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

视觉 - 惯性 - 车轮计量与滑动补偿和动态特征消除.

Niraj Reginald1, Omar Al-Buraiki1, Thanacha Choopojcharoen1

  • 1Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法来改善机器人导航,通过使用先进的机器学习技术来补偿视觉惯性轮距测量 (VIWO) 中的轮子滑动,以实现更准确的定位.

关键词:
动态环境 导航 导航多状态约束 卡尔曼过多传感器融合融合技术视觉-惯性-车轮测距仪轮子滑动补偿 轮子滑动补偿

更多相关视频

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K
Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
08:55

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Published on: February 5, 2020

7.4K

相关实验视频

Last Updated: May 21, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K
Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
08:55

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Published on: February 5, 2020

7.4K

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 传感器融合式传感器

背景情况:

  • 机器人定位和测距因传感器不确定性和轮子滑动而面临挑战.
  • 视觉惯性轮距测量 (VIWO) 结合了多个传感器输入以实现强大的导航.
  • 现有的VIWO系统在具有挑战性的地形和动态环境中难以提供准确的性能.

研究的目的:

  • 开发一种新的数据驱动方法来补偿VIWO系统中的轮子滑动.
  • 为了提高机器人定位和测距的准确性和稳定性.
  • 通过解决动态特征点来改善视觉和惯性测量的集成.

主要方法:

  • 使用高斯过程回归 (GPR) 与深度内核学习来建模和减轻滑动诱导的错误.
  • 嵌入了长短期内存 (LSTM) 层,用于高级错误建模.
  • 开发了一个特征信任估计器来处理视觉数据中的动态特征点.
  • 采用多状态约束卡尔曼波器 (MSCKF) 来进行状态估计.

主要成果:

  • 拟议的方法有效地弥补了轮子的滑动,大大提高了定位的准确性.
  • 该系统在具有挑战性的地形和动态环境中表现出增强的稳定性.
  • 与传统的VIWO系统相比,GPR和LSTM层的集成带来了更高的性能.
  • 实验验证证了使用真实世界数据集的方法的有效性.

结论:

  • 这种新的数据驱动方法有效地解决了VIWO中的轮子滑动和动态特征点问题.
  • 增强的VIWO系统为自主导航提供了更高的准确性和稳定性.
  • 这项研究有助于推进用于不可预测环境的多传感器融合和导航技术.