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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

400
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...
400
Observational Learning01:12

Observational Learning

166
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
166
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

459
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...
459
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

323
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...
323
Reinforcement01:23

Reinforcement

202
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
202
Reinforcement Schedules01:24

Reinforcement Schedules

144
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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相关实验视频

Updated: Jun 26, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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基于回落的地平线强化学习的USV轨迹跟踪控制

Yinghan Wen1, Yuepeng Chen1, Xuan Guo2

  • 1School of Automation, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
概括

我们开发了一种新的回落地平线强化学习 (RHRL) 方法,用于无人地面车辆 (USV) 的精确轨迹跟踪控制. 这种方法使得离线和在线学习能够实现最佳的控制策略.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统 控制系统
  • 人工智能的人工智能

背景情况:

  • 无人驾驶地面车辆 (USV) 需要精确的轨迹跟踪才能有效运行.
  • 现有的控制方法,如利亚普诺夫模型预测控制 (LMPC) 和滑动模式控制 (SMC) 有局限性.
  • 在永恒时间领域的最佳控制带来了重大挑战.

研究的目的:

  • 为高精度的USV轨迹跟踪引入一种新的回落地平线强化学习 (RHRL) 方法.
  • 开发一个控制架构,将前和反组件结合起来,以提高性能.
  • 为了证明RHRL对传统控制方法的优势.

主要方法:

  • 设计了一个复合控制架构,具有前 (路径曲率,动态模型) 和反 (RHRL) 组件.
  • 该RHRL算法与滚动时间域优化机制集成.
  • 在预测领域内,用于价值函数和控制策略学习的时间独立的执行评估员网络.
  • 提供了RHRL算法收和闭环系统稳定性的理论证明.

主要成果:

  • RHRL控制器提供了一个明确的状态反控制规律,促进了直接的线下和在线学习.
  • 提出的方法有效地将永恒的最佳控制问题转化为可解决的有限时间域问题.
关键词:
执行官 评估员 评估员回落的地平线强化学习的学习.轨迹跟踪 轨迹跟踪 轨迹跟踪无人驾驶地表车辆 无人驾驶地表车辆

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  • 模拟测试证实了RHRL方法对USV轨迹控制的有效性.
  • 结论:

    • 新的RHRL方法为精确的USV轨迹跟踪提供了强大的和可适应的解决方案.
    • 该方法在线和离线学习的能力增强了其实际适用性.
    • 与LMPC和SMC相比,RHRL表现出优越的性能和学习能力.