Collective dynamics of intelligent active Brownian particles with visual perception and velocity alignment in 3D: spheres, rods, and worms

Zhaoxuan Liu, Marjolein Dijkstra*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Many living systems, such as birds and fish, exhibit collective behaviors like flocking and swarming. Recently, an experimental system of active colloidal particles has been developed, where the motility of each particle is adjusted based on its visual detection of surrounding particles. These particles with visual-perception-dependent motility exhibit group formation and cohesion. Inspired by these behaviors, we investigate intelligent active Brownian particles (iABPs) equipped with visual perception and velocity alignment in three dimensions using computer simulations. The visual-perception-based self-steering describes the tendency of iABPs to move toward the center of mass of particles within their visual cones, while velocity alignment encourages alignment with neighboring particles. We examine how the behavior varies with the visual cone angle θ, self-propulsion speed (Péclet number Pe), and the interaction strengths of velocity alignment (Ωa) and visual-based self-steering (Ωv). Our findings show that spherical iABPs form dense clusters, worm-like clusters, milling behaviors, and dilute-gas phases, consistent with 2D studies. By reducing the simulation box size, we observe additional structures like band-like clusters and dense baitball formations. Additionally, rod-like iABPs form band-like, worm-like, radiating, and helical structures, while iABP worms exhibit band-like, streamlined, micellar-like and entangled structures. Many of these patterns resemble collective behaviors in nature, such as ant milling, fish baitballs, and worm clusters. Advances in synthetic techniques could enable nanorobots with similar capabilities, offering insights into multicellular systems through active matter.

Original languageEnglish
Pages (from-to)1529-1544
Number of pages16
JournalSoft Matter
Volume21
Issue number8
Early online date2025
DOIs
Publication statusPublished - 28 Feb 2025

Bibliographical note

Publisher Copyright:
© 2025 The Royal Society of Chemistry.

Funding

M. D. acknowledges funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. ERC-2019-ADG 884902 SoftML). We also acknowledge financial support from NWO and from Canon Production Printing Netherlands B.V., FIP2 project KICH2.V4C.20.001.

FundersFunder number
HORIZON EUROPE European Research CouncilERC-2019-ADG 884902 SoftML
European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme
NWOFIP2 project KICH2.V4C.20.001
Canon Production Printing Netherlands B.V.

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