Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

429
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
429
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

357
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...
357
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

992
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...
992
Distance Measurements by Taping01:18

Distance Measurements by Taping

332
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
332
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

445
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
445
Cluster Sampling Method01:20

Cluster Sampling Method

13.8K
Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bidirectional-thruster multirotor for perimeter pipe inspections (BiMPPI): A nonlinear optimal integral-SDRE design.

ISA transactions·2025
Same author

eFFT: An Event-Based Method for the Efficient Computation of Exact Fourier Transforms.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Exponential and robust position-constrained control of robot manipulators via diffeomorphisms.

ISA transactions·2023
Same author

Closed-loop nonlinear optimal control design for flapping-wing flying robot (1.6 m wingspan) in indoor confined space: Prototyping, modeling, simulation, and experiment.

ISA transactions·2023
Same author

How ornithopters can perch autonomously on a branch.

Nature communications·2022
Same author

Aeroelastics-aware compensation system for soft aerial vehicle stabilization.

Frontiers in robotics and AI·2022

Related Experiment Video

Updated: Dec 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.9K

An Efficient Distributed Area Division Method for Cooperative Monitoring Applications with Multiple UAVs.

José Joaquín Acevedo1, Ivan Maza1, Anibal Ollero1

  • 1GRVC Robotics Lab, University of Seville, Escuela Superior de Ingenieros, Avenida de los Descubrimientos s/n, 41092 Seville, Spain.

Sensors (Basel, Switzerland)
|June 24, 2020
PubMed
Summary

This study presents a distributed algorithm for cooperative Unmanned Aerial Vehicle (UAV) missions, optimizing area division for efficient monitoring. The frequency-based approach ensures rapid convergence to the best solution.

Keywords:
area divisioncoordination variablesdistributed systemfrequency-based approachmonitoringmulti-UAVunmanned aerial vehicles

More Related Videos

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.8K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

310

Related Experiment Videos

Last Updated: Dec 17, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.9K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.8K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

310

Area of Science:

  • Robotics and Control Systems
  • Distributed Systems
  • Cooperative Mission Planning

Background:

  • Cooperative monitoring missions with multiple Unmanned Aerial Vehicles (UAVs) face challenges in efficient area division.
  • Existing distributed methods may lack optimal convergence speed for dynamic environments.

Purpose of the Study:

  • To develop a distributed online algorithm for optimal area division in multi-UAV cooperative monitoring.
  • To accelerate the convergence of area division to an optimal solution using a frequency-based approach.

Main Methods:

  • A distributed online algorithm is proposed, starting from a sub-optimal area division.
  • The algorithm utilizes a frequency-based approach and 'coordination variables' for information sharing among neighboring UAVs (left, right, above, below).
  • A distributed division protocol is defined to coherently determine sub-area size and shape for each UAV.

Main Results:

  • The proposed solution theoretically achieves convergence time linearly dependent on the number of UAVs.
  • Validation results demonstrate the algorithm's performance against other distributed techniques based on convergence time.

Conclusions:

  • The developed distributed algorithm effectively addresses the area division problem for cooperative UAV monitoring.
  • The frequency-based approach enhances convergence speed, offering a promising solution for efficient multi-UAV operations.