Collective behavior of self-steering active particles with velocity alignment and visual perception

Abstract

The formation and dynamics of swarms is widespread in living systems, from bacterial biofilms to schools of fish and flocks of birds. We study this emergent collective behavior via agent-based simulations in a model of active Brownian particles with visual-perception-based steering and alignment interactions. The dynamics, shape, and internal structure of the emergent aggregates, clusters, and swarms of these intelligent active Brownian particles are determined by the maneuverabilities Ωv and Ωa, quantifying the steering based on the visual signal and polar alignment, respectively, the propulsion velocity, characterized by the Péclet number Pe, the vision angle θ, and the orientational noise. Various nonequilibrium dynamical aggregates—like motile wormlike swarms and milling, and close-packed or dispersed clusters—are obtained. Small vision angles imply the formation of small clusters, while large vision angles lead to more complex clusters. In particular, a strong polar-alignment maneuverability Ωa favors elongated wormlike swarms, which display superdiffusive motion over a much longer time range than individual ABPs, whereas a strong vision-based maneuverability Ωv favors compact, nearly immobile aggregates. Swarm trajectories show long persistent directed motion, interrupted by sharp turns. Milling rings, where a wormlike swarm bites its own tail, emerge for an intermediate regime of Pe and vision angles. Our results offer insights into the behavior of animal swarms and provide design criteria for swarming microbots. Published by the American Physical Society 2024