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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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A gyroscope is defined as a spinning disk in which the axis of rotation is free to assume any orientation. When spinning, the orientation of the spin axis is unaffected by the orientation of the body that encloses it. The body or vehicle enclosing the gyroscope can be moved from place to place, while the orientation of the spin axis remains the same. This makes gyroscopes very useful in navigation, especially where magnetic compasses cannot be used, such as in crewed and crewless spacecraft,...
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无人机传感器故障数据集:生物米萨 (Biomisa) 飞行器传感批判 (BASiC)

Muhammad Waqas Ahmad1, Muhammad Usman Akram1

  • 1Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.

Data in brief
|February 2, 2024
PubMed
概括

一个名为Biomisa Arducopter Sensory Critique (BASiC) 的新数据集解决了无人机传感器故障的问题. 该资源有助于开发可靠的深度学习模型,以实现更安全的自动飞行.

关键词:
在ArduPilot中使用ArduPilot.自动驾驶飞行自动驾驶飞行自动驾驶飞行员可以自动驾驶.任务规划员 任务规划员传感器故障数据集在循环中的软件.无人机无人机无人机是什么?无人驾驶飞行器是一种无人驾驶飞行器.

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科学领域:

  • 机器人技术和自主系统
  • 航空航天工程 航空航天工程
  • 数据科学数据科学数据科学

背景情况:

  • 无人机依赖传感器数据进行导航和控制.
  • 传感器故障或网络攻击改变数据可能导致不安全的飞行条件和机.
  • 现有的数据集缺乏全面的无人机传感器故障分析能力.

研究的目的:

  • 为无人机传感器故障分析引入Biomisa Arducopter感觉批判 (BASiC) 数据集.
  • 为开发和测试无人机自动驾驶器故障处理机制提供资源.
  • 通过先进的数据分析,提高自主无人机操作的安全性和可靠性.

主要方法:

  • ArduPilot平台被用于与软件在循环 (SITL) 模拟的实验.
  • 创建了BASiC数据集,包括70个自主飞行 (超过7小时) 与故障前,故障后和无故障数据.
  • 模拟包括六个关键传感器故障:GPS,遥控器,加速度计,陀螺仪,指南针和气压计.

主要成果:

  • 对于每个模拟传感器故障场景,BASiC数据集提供了超过3小时的故障前和故障后数据.
  • 它在各种故障条件下提供了一个全面的时间序列传感器数据集合.
  • 该数据集可以对传感器性能退化和恢复进行详细分析.

结论:

  • 该BASiC数据集是研究无人机传感器故障研究的研究社区的宝贵资源.
  • 它促进了深度学习模型的开发和验证,用于自主系统中的时间序列信号分析.
  • 这一数据集可以大大有助于提高任务关键无人机飞行的安全性和可靠性.