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相关概念视频

Velocity and Position by Integral Method01:13

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If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
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一种通过位置校正模型和AUV速度模型辅助的集成导航方法.

Pengfei Lv1, Junyi Lv2, Zhichao Hong1,3

  • 1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了自动水下车辆 (AUV) 的新导航方法,该方法结合了速度模型和位置校正模型. 当GPS无法使用时,这种方法显著提高了水下导航的准确性.

关键词:
自主水下车辆自主水下车辆扩展的卡尔曼过器位置校正模型的位置校正模型.水下航行水下航行速度模型的速度模型.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 海洋学 海洋学 海洋学
  • 人工智能的人工智能

背景情况:

  • 自主水下车辆 (AUV) 面临导航挑战,因为水下没有GPS.
  • 传统的导航系统,如扩展卡尔曼波器 (EKF),在没有位置辅助的情况下会出现累积的错误.

研究的目的:

  • 为AUV开发一种创新的导航方法,以提高在没有GPS的水下环境中的准确性.
  • 通过改进定位来提高AUV任务的可靠性和准确性.

主要方法:

  • 使用动态模型和最佳修剪极端学习机器 (OP-ELM) 开发了一种在线训练的速度模型.
  • 使用混合门循环神经网络 (HGRNN) 构建了一个位置校正模型 (PCM) 来校正AUV位置.
  • 将速度和位置校正模型与EKF集成,用于增强导航 (VM-PCM-EKF算法).

主要成果:

  • 拟议的VM-PCM-EKF算法显著提高了AUV定位精度.
  • 与传统EKF算法相比,实现了87.2%的最大精度改进.
  • 在多普勒速度日志 (DVL) 更新间隔期间证明了更一致和可靠的速度估计.

结论:

  • 集成的速度和位置校正模型有效地减轻GPS不可用的水下场景中的导航错误.
  • 拟议的方法提高了AUV操作的可靠性,使更复杂和更长时间的水下任务成为可能.
  • 这种方法为在具有挑战性的水下环境中精确的AUV导航提供了强大的解决方案.