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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

437
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...
437
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
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...
1.3K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.4K
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...
4.4K
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

924
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
924
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.5K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.5K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.5K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Exponential synchronization of T-S fuzzy complex-valued BAM neural networks with mixed time-varying delays via event-triggered control engineering and applications.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Real-time sparse signal reconstruction via KKT-conditions-driven analog circuit solver.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models With Temporal Distillation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Stability Switching and Oscillation Regulation Strategies for Large-Scale Fractional-Order Neural Networks With Double Hubs and Multiple Delays.

IEEE transactions on cybernetics·2026
Same author

Resilient State and Input Estimation for Complex Network Subject to Cyber Attack: A Set-Membership Method.

IEEE transactions on cybernetics·2026
Same journal

Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders.

Cognitive computation·2026
Same journal

Neurodynamical Computing at the Information Boundaries of Intelligent Systems.

Cognitive computation·2024
Same journal

Large-Kernel Attention for 3D Medical Image Segmentation.

Cognitive computation·2024
Same journal

Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.

Cognitive computation·2023
Same journal

Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus.

Cognitive computation·2023
Same journal

Deep Learning Based Traffic Prediction Method for Digital Twin Network.

Cognitive computation·2023
See all related articles

Related Experiment Video

Updated: Apr 24, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

10.9K

A Consensus-Based Grouping Algorithm for Multi-agent Cooperative Task Allocation with Complex Requirements.

Simon Hunt1, Qinggang Meng1, Chris Hinde1

  • 1Department of Computer Science, Loughborough University, Loughborough, UK.

Cognitive Computation
|September 6, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced consensus algorithm for unmanned aerial vehicle (UAV) cooperation, inspired by social insects. It improves task allocation efficiency and reduces communication overhead in complex, dynamic environments.

Keywords:
Cognitive behavioursConsensusCooperationEusocial animalsTask allocation

More Related Videos

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

12.6K

Related Experiment Videos

Last Updated: Apr 24, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

10.9K
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

12.6K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Multi-agent Systems

Background:

  • Cooperative tasks in multi-agent systems, particularly with unmanned aerial vehicles (UAVs), present significant challenges in efficient task allocation and coordination.
  • Existing consensus algorithms often struggle with complex task dependencies, equipment requirements, and dynamic environmental changes.

Purpose of the Study:

  • To extend the consensus-based bundle algorithm for enhanced multi-agent task cooperation in UAVs.
  • To develop a decentralized algorithm inspired by eusocial animal behavior for adaptive task assignment.
  • To improve algorithm efficiency, handle task complexity, and reduce communication costs.

Main Methods:

  • Extension of the consensus-based bundle algorithm.
  • Incorporation of cognitive behaviors from eusocial animals (bees, ants) for decentralized decision-making.
  • Development of a novel data structure to decrease communication costs.
  • Addressing heterogeneous agents, deadlocking, and dynamic assignment storage.

Main Results:

  • The proposed algorithm converges to a conflict-free, feasible solution, outperforming previous methods.
  • Demonstrated reduction in data usage and communication time for multi-agent task consensus.
  • Successfully accounts for heterogeneous agents, task dependencies, and dynamic environments.

Conclusions:

  • The enhanced consensus algorithm provides an efficient and robust solution for cooperative tasks among UAVs.
  • The bio-inspired approach effectively manages complex task requirements and environmental dynamics.
  • Significant improvements in communication efficiency and task assignment were achieved.