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Related Experiment Video

Updated: Sep 3, 2025

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

A Locust-Inspired Model of Collective Marching on Rings.

Michael Amir¹, Noa Agmon², Alfred M Bruckstein¹

¹Department of Computer Science, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Entropy (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

Autonomous agents in a ring environment achieve local consensus on direction, inspired by locust swarms. With random track-jumping, global consensus is reached, demonstrating swarm intelligence principles.

Keywords:

crowd dynamics locusts mobile robotics natural algorithms swarms

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Area of Science:

Collective behavior
Swarm intelligence
Robotics

Background:

Locust swarms exhibit emergent collective motion in laboratory settings.
Autonomous agents can be designed to mimic biological swarm dynamics.
Understanding consensus in multi-agent systems is crucial for coordination.

Purpose of the Study:

To investigate the emergence of directional consensus in a stochastic swarm of locust-inspired agents.
To determine the time scales for consensus formation in a ring-like environment.
To explore the impact of track-switching and random behavior on global consensus.

Main Methods:

Mathematical modeling of agent interactions and dynamics.
Analysis of agent movement on a discretized, multi-track ring environment.
Derivation of asymptotic bounds for consensus time.
Numerical simulations to validate theoretical predictions.

Main Results:

Agents converge to a local consensus, marching in the same direction on each track.
The time to reach local consensus depends on agent number, track length, and number of tracks.
A small probability of random track-jumping leads to global consensus across all tracks.

Conclusions:

The study confirms that locust-inspired agents can achieve directional consensus.
The findings provide theoretical bounds and simulation-based evidence for swarm stabilization.
Introducing controlled randomness can facilitate global coordination in multi-agent systems.