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相关概念视频

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
Mechanical Systems01:22

Mechanical Systems

Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically described...

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相关实验视频

Updated: Jun 21, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

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在基于合作合规控制的多机器人精密组装中获得技能框架.

Xiaogang Song1, Peng Xu1, Wenfu Xu1

  • 1School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, PR China; Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Harbin Institute of Technology, Shenzhen 518055, PR China.

ISA transactions
|October 18, 2024
PubMed
概括

这项研究引入了多机器人组装的新框架,提高了复杂,紧密结合的任务的学习效率. 它能够高精度的机器人组装,尽管干扰和没有事先的知识.

关键词:
多机器人合作集成组合.多机器人合作操纵器多机器人合作操纵器强化学习是一种强化学习.技能获取 技能获得 技能获取

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Operation of the Collaborative Composite Manufacturing CCM System
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科学领域:

  • 机器人和自动化 机器人和自动化
  • 制造业 工程 制造工程
  • 人工智能的人工智能

背景情况:

  • 高精度机器人组装在制造业中至关重要,但受到干扰的挑战.
  • 现有的研究主要涉及单一或弱合的多机器人系统.
  • 紧密合的多机器人组件具有显著的不确定性是未被充分探索的.

研究的目的:

  • 为复杂,紧密结合的多机器人组装任务提出一个高效的技能获取框架.
  • 为了提高机器人组装系统的学习效率.
  • 为了应对合作机器人组装中不确定的干扰所带来的挑战.

主要方法:

  • 集成双环合力位置控制 (DLCFPC) 算法,用于同时控制运动和力.
  • 实施并行技能学习算法以加快技能获取.
  • 在框架内包括碰撞检测机制.

主要成果:

  • 拟议的框架使多机器人系统能够实现高精度组装.
  • 在合作任务中表现出对各种干扰的强度.
  • 成功执行复杂的任务,如没有事先知识的插孔组装.

结论:

  • 开发的框架显著提高了挑战机器人组装的学习效率.
  • 它为面对不确定性的紧密合的多机器人系统提供了强大的解决方案.
  • 这种方法在复杂的制造场景中促进了高精度合作装配.