Jove
Visualize
联系我们
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

相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

394
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...
394
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.0K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
12.0K
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

415
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
415
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.7K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

452
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...
452
Equation of Motion: General Plane motion - Problem Solving01:16

Equation of Motion: General Plane motion - Problem Solving

174
Consider a lawn roller with a mass of 100 kg, a radius of 0.2 meters, and a radius of gyration of 0.15 meters. A force of 200 N is applied to this roller, angled at 60 degrees from the horizontal plane. What will be the angular acceleration of the lawn roller?
The friction between the roller and the ground is characterized by two coefficients. The static friction coefficient is 0.15, while the kinetic friction coefficient is 0.1. These values are crucial in understanding the interaction between...
174

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 rare case of intervertebral disc calcification combined with ossification of the posterior longitudinal ligament in a child: a case report and literature review.

BMC musculoskeletal disorders·2024
Same author

6'-<i>O</i>-Caffeoylarbutin from Quezui Tea: A Highly Effective and Safe Tyrosinase Inhibitor.

International journal of molecular sciences·2024
Same author

Regulation of polysaccharide in Wu-tou decoction on intestinal microflora and pharmacokinetics of small molecular compounds in AIA rats.

Chinese medicine·2024
Same author

High-risk characteristics of pathological stage I lung adenocarcinoma after resection: patients for whom adjuvant chemotherapy should be performed.

Heliyon·2023
Same author

XAF1 promotes colorectal cancer metastasis via VCP-RNF114-JUP axis.

The Journal of cell biology·2023
Same author

Polydopamine nanomaterials and their potential applications in the treatment of autoimmune diseases.

Drug delivery·2023
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

相关实验视频

Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

在复杂的城市环境中用于自动驾驶汽车的基于时空空间联合优化的轨迹规划方法.

Jianhua Guo1, Zhihao Xie1, Ming Liu2

  • 1State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究提出了一种新的时空联合优化轨迹规划 (SJOTP) 方法,用于在复杂的城市环境中导航的自动驾驶汽车. 该方法通过整合多约束优化和避免碰撞算法来确保安全和高效的路径.

关键词:
自动驾驶汽车的导航避免碰撞,避免碰撞.运动规划 运动规划

更多相关视频

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

1.9K

相关实验视频

Last Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

1.9K

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
  • 自主系统 自主系统

背景情况:

  • 安全和高效的轨道规划对于在动态的城市环境中实现自动驾驶至关重要.
  • 具有多种约束的复杂环境挑战现有的行为规划者,可能导致不安全的轨迹.
  • 目前的方法在复杂的城市场景中难以保证无碰撞路径.

研究的目的:

  • 为复杂的城市环境引入基于时空联合优化的轨迹规划 (SJOTP) 方法.
  • 为应对多约束轨迹规划的挑战,确保安全和效率.
  • 开发一款用于自动驾驶汽车导航的强大解决方案.

主要方法:

  • 一个行为规划器生成初始轨迹集群.
  • 在一个膨胀的地图上,以拓为导向的混合A*算法减轻了静态障碍物碰撞.
  • 多限制,多目标的轨迹规划考虑了障碍物,道路附着性和车辆动态,使用差异平度和点质量模型.
  • 一个时空关节优化解决方案处理纵向和横向合.

主要成果:

  • 在模拟中,SJOTP方法成功生成了最佳轨迹.
  • 该方法展示了安全和高效的路线规划能力.
  • 在多代理模拟平台上的验证证实了该方法的有效性.

结论:

  • 拟议的SJOTP方法为复杂的城市环境中安全和高效的轨迹规划提供了可行的解决方案.
  • 多重约束优化和先进算法的集成增强了自动驾驶汽车的导航.
  • 这项研究有助于推进可靠的自动驾驶系统.