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相关概念视频

Equilibrium and Balance01:15

Equilibrium and Balance

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The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...
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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.
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The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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混合监督和强化学习用于自动驾驶汽车的运动疾病意识路径跟踪.

Yukang Lv1, Yi Chen1, Ziguo Chen1

  • 1College of Automotive Engineering, Jilin University, Changchun 130025, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括

本研究介绍了一种混合监督强化学习 (HSRL) 框架,用于自动驾驶路径跟踪. HSRL提高了跟踪精度和计算效率,同时显著减少了乘客的运动病 (MS).

关键词:
自动驾驶汽车是自动驾驶的运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness路径跟踪跟踪路径跟踪强化学习是一种强化学习.监督学习学习监督学习

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 路径跟踪对于自动驾驶 (AD) 的安全,舒适和效率至关重要.
  • 现有的方法难以平衡跟踪精度与计算成本,并且没有解决运动性疾病 (MS).
  • 随着乘客从事非驾驶活动,MS在AD中越来越令人担忧.

研究的目的:

  • 开发一个新的框架,混合监督强化学习 (HSRL),用于AD路径跟踪.
  • 为了减少乘客的不适和运动性疾病 (MS),同时保持高跟踪精度和计算效率.
  • 为了解决当前路径跟踪控制器的局限性.

主要方法:

  • 实施了混合监督强化学习 (HSRL) 框架.
  • 利用专家数据引导的监督学习来优化路径跟踪模型,解决强化学习 (RL) 样本效率的问题.
  • 将乘客MS机制集成到RL的多目标奖励功能中,以增强强性和舒适性.

主要成果:

  • 与比例整数导数 (PID) 和模型预测控制 (MPC) 相比,HSRL框架显示出更高的性能.
  • 实现了高精度路径跟踪,并提高了计算效率.
  • 在各种场景中显著降低了乘客的累积运动恶性剂量值 (MSDV).

结论:

  • HSRL有效地平衡了高精度路径跟踪与在自动驾驶中优化乘客舒适度.
  • 拟议的框架为AD路径跟踪提供了一个计算效率高的解决方案.
  • HSRL提出了一种有希望的方法来缓解自动驾驶汽车的运动病.