A Unified Stochastic Mechanism Underlying Collective Behavior in Ants, Physical Systems, and Robotic Swarms

TL;DR

This paper proposes a unified stochastic model explaining collective behaviors across ants, physical systems, and robotic swarms, based on maximization principles under energy constraints, enabling scalable decentralized cooperation.

Contribution

It introduces a shared statistical mechanism underlying biological, physical, and robotic swarms, bridging gaps between these domains with empirical and robotic evidence.

Findings

Shared stochastic mechanism observed in ants and physical systems

Robotic swarms can mimic physical phase-like behaviors

Unified model enables scalable decentralized cooperation

Abstract

Biological swarms, such as ant colonies, achieve collective goals through decentralized and stochastic individual behaviors. Similarly, physical systems composed of gases, liquids, and solids exhibit random particle motion governed by entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain the stochastic behavior in both biological and physical systems. Here, we present empirical evidence from \textit{Formica polyctena} ants that reveals a shared statistical mechanism underlying both systems: maximization under different energy function constraints. We further demonstrate that robotic swarms governed by this principle can exhibit scalable, decentralized cooperation, mimicking physical phase-like behaviors with minimal individual computation. These findings established a unified stochastic model linking biological, physical, and robotic swarms, offering a scalable principle for designing robust and intelligent swarm robotics.