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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

657
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...
657
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

490
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...
490
Uncertainty: Overview00:59

Uncertainty: Overview

529
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.
529
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

636
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
636

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相关实验视频

Updated: Jun 15, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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进化优化用于在不确定的环境中对风险有意识的异质多代理路径规划.

Fatemeh Rekabi Bana1, Tomáš Krajník2, Farshad Arvin1

  • 1Swarm and Computational Intelligence Laboratory (SwaCIL), Department of Computer Science, Durham University, Durham, United Kingdom.

Frontiers in robotics and AI
|August 28, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多代理路径规划方法,用于合作机器人. 该算法确保了对探索各种环境的团队安全,无碰撞的轨迹,优化了探索和数据收集.

关键词:
生物混合系统是生物混合系统.避免碰撞,避免碰撞.遗传优化 - 遗传优化多个代理的多个代理.路径规划路径规划路径规划一个概率的路线图.

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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
  • 控制系统 控制系统

背景情况:

  • 合作型多代理系统使微型机器人能够在各种环境中收集数据和互动.
  • 现有的路径规划方法往往难以避免碰撞,并为多个代理商进行协调探索.

研究的目的:

  • 提出一种新的多代理路径规划方法,用于生成无碰撞的轨迹.
  • 为了使合作机器人能够探索环境,作为有效数据收集和危险检测的形成.

主要方法:

  • 利用风险意识的概率路线图算法来生成地图.
  • 使用节点分类来定义勘探区域.
  • 使用定制的遗传算法进行组合优化.

主要成果:

  • 该算法计算安全轨迹,最大限度地减少行程距离和碰撞概率.
  • 它考虑了代理人的动态行为和环境的不确定性.
  • 绩效在不同的群体规模和基准场景中得到验证.

结论:

  • 提议的优化方法表明,无论集团规模如何,都具有稳定和收的特性.
  • 它为多代理系统促进协调的勘探和可靠的数据采集.