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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Related Experiment Video

Updated: Dec 22, 2025

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles

Chengke Xiong1,2, Hexiong Zhou1,2, Di Lu1,2

  • 1School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)
|May 6, 2020
PubMed
Summary

This study introduces a new algorithm for unmanned marine vehicles (UMVs) to efficiently gather scientific data. The RAST* algorithm optimizes paths for information gathering while avoiding obstacles and meeting mission time.

Keywords:
adaptive ocean samplingpath planningrapidly-exploring adaptive sampling tree starunmanned marine vehicles

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Area of Science:

  • Robotics
  • Oceanography
  • Artificial Intelligence

Background:

  • Adaptive sampling is crucial for efficient data collection in marine environments.
  • Unmanned Marine Vehicles (UMVs) require sophisticated path planning for autonomous scientific missions.
  • Existing algorithms face challenges in optimizing information gain while adhering to mission constraints.

Purpose of the Study:

  • To develop a novel sample-based path planning algorithm for UMVs.
  • To maximize information gathering in a scientific interest area.
  • To ensure collision avoidance and meet mission time constraints.

Main Methods:

  • The proposed algorithm, rapidly-exploring adaptive sampling tree star (RAST*), integrates RRT* concepts with tournament selection and informative heuristics.
  • RAST* operates in continuous space for efficient data searching.
  • Algorithm performance was evaluated through numerical simulations and field experiments.

Main Results:

  • RAST* demonstrated superior performance compared to RRST*, RAST, and PSO.
  • The algorithm effectively maximizes information gathering for scientific missions.
  • Successful proof-of-concept field experiments validated the algorithm's effectiveness.

Conclusions:

  • The RAST* algorithm offers an effective solution for adaptive sampling path planning in marine robotics.
  • It provides a near-optimal path for UMVs, enhancing scientific data acquisition.
  • RAST* represents a significant advancement in autonomous underwater exploration.