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

Optimal Foraging00:48

Optimal Foraging

11.7K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
11.7K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.5K
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...
4.5K
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

927
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...
927
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
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

1.2K
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
1.2K
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

Rethinking hierarchy: the auditory system as an integrated cortical-subcortical network.

Nature reviews. Neuroscience·2026
Same author

Dominant baboons experience more interrupted and less rest at night.

Current biology : CB·2025
Same author

A FHIR-Powered Python Implementation of the SENECA Algorithm for Sepsis Subtyping.

Applied clinical informatics·2025
Same author

Effects of exposure to pandemic-related stressors on anxiety and mood difficulty during versus before the COVID-19 pandemic in United States Army soldiers and veterans.

Nature. Mental health·2025
Same author

Geospatial Socioeconomic Indicators and Penicillin Allergy Delabeling in Primary Care Patients.

JAMA network open·2025
Same author

Alkene-Activating Effect in Molybdenum-Catalyzed Asymmetric Hydrogenation of Arenes: Insights into Activity and Selectivity.

Journal of the American Chemical Society·2025
Same journal

RNA-ligand complexes and the attenuation of neutral confinement in the evolution of RNA secondary structures.

Journal of the Royal Society, Interface·2026
Same journal

Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.

Journal of the Royal Society, Interface·2026
Same journal

Thermal stress disrupts symbiotic fluid dynamics in bobtail squid.

Journal of the Royal Society, Interface·2026
Same journal

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same journal

Slow modulation of the contraction patterns in Physarum polycephalum.

Journal of the Royal Society, Interface·2026
Same journal

Moo-ving mountains: grazing agents drive terracette formation on steep hillslopes.

Journal of the Royal Society, Interface·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

14.0K

Solving the shepherding problem: heuristics for herding autonomous, interacting agents.

Daniel Strömbom1, Richard P Mann2, Alan M Wilson3

  • 1Department of Mathematics, Uppsala University, Uppsala 75106, Sweden strombom@math.uu.se.

Journal of the Royal Society, Interface
|August 29, 2014
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel algorithm for animal herding, mimicking dog behavior to control group movement. This adaptive strategy switches between collecting and driving agents, applicable to robotics and crowd control.

Keywords:
agent-based modelcollective motionsheepsheepdog

More Related Videos

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 Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

1.6K

Related Experiment Videos

Last Updated: Apr 25, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

14.0K
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 Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

1.6K

Area of Science:

  • Robotics
  • Animal Behavior
  • Collective Dynamics

Background:

  • The 'shepherding problem' involves controlling a group's movement, exemplified by dogs herding sheep.
  • Understanding the underlying algorithms used by herding dogs is crucial for broader applications.
  • Existing methods lack a generalizable algorithm for influencing collective motion.

Purpose of the Study:

  • To demonstrate a general algorithm for the shepherding problem.
  • To replicate observed sheep-dog interaction dynamics.
  • To inform the design of robots for agent control.

Main Methods:

  • Developed an adaptive algorithm switching between 'collecting' and 'driving' behaviors.
  • Validated the algorithm against empirical data from sheep-dog interactions.
  • Simulated agent-based modeling to analyze collective movement patterns.

Main Results:

  • The proposed algorithm successfully reproduced key features of sheep-dog herding.
  • Demonstrated effective control over dispersed and aggregated groups of agents.
  • Identified specific behavioral switches crucial for successful shepherding.

Conclusions:

  • A generalizable algorithm for the shepherding problem has been identified.
  • The algorithm provides a framework for designing autonomous systems, like robots, to manage collective movement.
  • Findings offer insights into animal behavior and potential applications in engineering and environmental management.