Small noise may diversify collective motion in Vicsek model

TL;DR

This paper rigorously analyzes the Vicsek model of collective motion under noise, revealing nontraditional phase transition behavior and providing a new method to predict system configurations and dynamics.

Contribution

It introduces a novel analytical method for Vicsek models, enabling rigorous analysis of phase transitions and complex behaviors in noisy self-propelled particle systems.

Findings

SPP systems switch infinitely between ordered and disordered states

Phase transition in Vicsek model is nontraditional

Method predicts configurations like turns, vortices, and flock mergers

Abstract

Natural systems are inextricably affected by noise. Within recent decades, the manner in which noise affects the collective behavior of self-organized systems, specifically, has garnered considerable interest from researchers and developers in various fields. To describe the collective motion of multiple interacting particles, Vicsek et al. proposed a well-known self-propelled particle (SPP) system, which exhibits a second-order phase transition from disordered to ordered motion in simulation; due to its non-equilibrium, randomness, and strong coupling nonlinear dynamics, however, there has been no rigorous analysis of such a system to date. To decouple systems consisting of deterministic laws and randomness, we propose a general method which transfers the analysis of these systems to the design of cooperative control algorithms. In this study, we rigorously analyzed the original Vicsek model under both open and periodic boundary conditions for the first time, and developed extensions to heterogeneous SPP systems (including leaderfollower models) using the proposed method. Theoretical results show that SPP systems switch an infinite number of times between ordered and disordered states for any noise intensity and population density, which implies that the phase transition indeed takes a nontraditional form. We also investigated the robust consensus and connectivity of these systems. Moreover, the findings presented in this paper suggest that our method can be used to predict possible configurations during the evolution of complex systems, including turn, vortex, bifurcation and flock merger phenomena as they appear in SPP systems.