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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
654
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

486
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
486
Uncertainty: Overview00:59

Uncertainty: Overview

526
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
526
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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相关实验视频

Updated: Jun 10, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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使用高斯过程预测安全机器人探索的不确定性规划.

Alex Stephens1, Matthew Budd1, Michal Staniaszek1

  • 1Oxford Robotics Institute, University of Oxford, Oxford, UK.

Autonomous robots
|October 18, 2024
PubMed
概括

这项研究引入了一个新的框架,用于在未知的环境中安全地进行机器人探索. 它使用高斯过程和马尔科夫决策过程来确保机器人在绘制新区域时保持在安全的操作限制内.

关键词:
斯过程是高斯过程.马尔科夫决策过程中的决策过程.移动机器人 移动机器人安全地进行勘探.

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相关实验视频

Last Updated: Jun 10, 2025

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 环境科学 环境科学

背景情况:

  • 移动机器人探索对于绘制未知区域的地图至关重要.
  • 确保机器人安全在预定义的环境条件值 (例如地形度,辐射) 内是一个重大挑战.
  • 现有的方法往往难以同时绘制地图和安全探索.

研究的目的:

  • 为移动机器人开发一种新的框架,用于在未知的环境中安全地探索.
  • 解决两个场景:已知的地图与未知的安全特征,和未知的地图与未知的安全特征.
  • 为了使机器人能够在遵守安全限制的同时构建地图.

主要方法:

  • 利用高斯过程来预测未访问地区的环境特征值.
  • 开发了一个马尔科夫决策过程,整合了高斯过程预测和环境模型过渡概率.
  • 将马尔科夫决策过程纳入一个探索算法,优先考虑信息获取,预测安全和近距离.

主要成果:

  • 拟议的框架有效地引导机器人探索新地区,同时保持安全.
  • 通过模拟进行的实证评估证明了该框架的有效性.
  • 在地下环境中的物理机器人上成功应用验证了这一方法.

结论:

  • 开发的框架为复杂,未知的环境中安全勘探提供了强大的解决方案.
  • 这种方法提高了机器人的自主性和在具有挑战性的地形上运营的安全性.
  • 预测建模和决策过程的整合为未来的机器人探索系统提供了一个有希望的方向.