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相关概念视频

Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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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...
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Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
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One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

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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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Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

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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...
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Time-Domain Interpretation of PD Control01:07

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Updated: Jan 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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工业机器人的多目标轨迹优化方法基于改进的TD3算法.

Yuhang Xu1, Shuhang Kong2, Xiaowu Kong3

  • 1State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.

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|November 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了工业机器人轨迹规划的新算法,优化了安全性,路径质量和狭窄空间中的速度. 与现有方法相比,该方法显著减少了执行时间.

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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
  • 控制系统 控制系统

背景情况:

  • 在狭窄的空间中运行的工业机器人在轨道规划方面面临挑战.
  • 同时优化避免碰撞,路径质量和执行时间是复杂的.
  • 现有的方法往往难以有效地平衡这些相互竞争的目标.

研究的目的:

  • 为工业机器人开发一个多目标的集成轨迹优化算法.
  • 为了增强轨迹的平滑性,算法融合和稳定性.
  • 为了尽量减少执行时间,同时确保无碰撞的路径.

主要方法:

  • 使用双延迟深度决定性政策梯度 (TD3) 强化学习框架.
  • 通过Butterworth过器和动态噪声减弱,改进了运动生成.
  • 采用基因算法进行超参数优化和优先重复体验.
  • 设计了一个基于时间距离信息的复合奖励函数.

主要成果:

  • 与RRT,手动教学,传统的TD3和SAC方法相比,实现了实际轨迹执行时间的显著减少.
  • 证明了分别减少了57.03%,22.94%,26.05%和20.5%.
  • 通过PyBullet中的模拟和使用Fairino Robot5臂的现实世界物理实验验证实了算法.

结论:

  • 拟议的多目标算法有效地优化了工业机器人在狭窄空间中的轨迹.
  • 综合方法成功地平衡了避免碰撞,路径质量和执行时间.
  • 这种方法比现有的轨道规划技术在效率上提供了显著的改进.