Large-Scale Patterns in a Minimal Cognitive Flocking Model: Incidental Leaders, Nematic Patterns, and Aggregates

2016/12/06 by Lucas Barberis, Fernando Peruani · 3 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Active matter #Active vision #Artificial intelligence #Classical mechanics #Collective behavior #Computer science #Distributed Control Multi-Agent Systems #Flocking (texture) #Liquid crystal #Mathematical analysis #Mathematics #Micro and Nano Robotics #Minimal model #Modular Robots and Swarm Intelligence #Optics #Pattern formation #Physics #Polar #Position (finance) #Statistical physics #cond-mat.soft #cond-mat.stat-mech #physics.bio-ph

paper · pdf · doi:10.1103/physrevlett.117.248001

published as Phys. Rev. Lett. 117, 248001 (2016)

openalex publication_date 2016/12/06 · arxiv created 2019/12/17 · arxiv updated 2019/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

We study a minimal cognitive flocking model, which assumes that the moving entities navigate using the available instantaneous visual information exclusively. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force, which acts inside a vision cone (VC), and lack velocity-velocity alignment. We show that this active system can exhibit-due to the VC that breaks Newton's third law-various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or millinglike patterns, the formation of moving-locally polar-files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and nonlinear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and nonliving active systems.

Citations

• Dynamic Clustering and Chemotactic Collapse of Self-Phoretic Active Particles
• Nonlinear Field Equations for Aligning Self-Propelled Rods
• Clusters, asters, and collective oscillations in chemotactic colloids
• Statistical Mechanics of Interacting Run-and-Tumble Bacteria
• Collective motion of self-propelled particles interacting without cohesion
• Active nematics on a substrate: Giant number fluctuations and long-time tails
• Enhanced Diffusion and Ordering of Self-Propelled Rods
• The Mechanics and Statistics of Active Matter
• Self-propelled particles with selective attraction–repulsion interaction: from microscopic dynamics to coarse-grained theories
• Collective motion

Cited by

• Defining and finding lifelike entities with a lazy filter
• Milling and meandering: Flocking dynamics of stochastically interacting agents with a field of view
• Emergence of rotating clusters in active Brownian particles with visual perception