Collective behavior of self-steering active particles with velocity alignment and visual perception
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866914818288517120 |
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| author | Negi, Rajendra Singh Winkler, Roland G. Gompper, Gerhard |
| author_facet | Negi, Rajendra Singh Winkler, Roland G. Gompper, Gerhard |
| contents | The formation and dynamics of swarms is wide spread in living systems, from bacterial bio-films to schools of fish and flocks of birds. We study this emergent collective behavior in a model of active Brownian particles with visual-perception-induced steering and alignment interactions through agent-based simulations. The dynamics, shape, and internal structure of the emergent aggregates, clusters, and swarms of these intelligent active Brownian particles (iABPs) is determined by the maneuverabilities $Ω_v$ and $Ω_a$, quantifying the steering based on the visual signal and polar alignment, respectively, the propulsion velocity, characterized by the P{é}clet number $Pe$, the vision angle $θ$, and the orientational noise. Various non-equilibrium dynamical aggregates -- like motile worm-like swarms and millings, 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 $Ω_a$ favors elongated worm-like swarms, which display super-diffusive motion over a much longer time range than individual ABPs, whereas a strong vision-based maneuverability $Ω_v$ favors compact, nearly immobile aggregates. Swarm trajectories show long persistent directed motion, interrupted by sharp turns. Milling rings, where a worm-like swarm bites its own tail, emerge for an intermediate regime of $Pe$ and vision angles. Our results offer new insights into the behavior of animal swarms, and provide design criteria for swarming microbots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_00670 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Collective behavior of self-steering active particles with velocity alignment and visual perception Negi, Rajendra Singh Winkler, Roland G. Gompper, Gerhard Soft Condensed Matter Biological Physics Computational Physics The formation and dynamics of swarms is wide spread in living systems, from bacterial bio-films to schools of fish and flocks of birds. We study this emergent collective behavior in a model of active Brownian particles with visual-perception-induced steering and alignment interactions through agent-based simulations. The dynamics, shape, and internal structure of the emergent aggregates, clusters, and swarms of these intelligent active Brownian particles (iABPs) is determined by the maneuverabilities $Ω_v$ and $Ω_a$, quantifying the steering based on the visual signal and polar alignment, respectively, the propulsion velocity, characterized by the P{é}clet number $Pe$, the vision angle $θ$, and the orientational noise. Various non-equilibrium dynamical aggregates -- like motile worm-like swarms and millings, 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 $Ω_a$ favors elongated worm-like swarms, which display super-diffusive motion over a much longer time range than individual ABPs, whereas a strong vision-based maneuverability $Ω_v$ favors compact, nearly immobile aggregates. Swarm trajectories show long persistent directed motion, interrupted by sharp turns. Milling rings, where a worm-like swarm bites its own tail, emerge for an intermediate regime of $Pe$ and vision angles. Our results offer new insights into the behavior of animal swarms, and provide design criteria for swarming microbots. |
| title | Collective behavior of self-steering active particles with velocity alignment and visual perception |
| topic | Soft Condensed Matter Biological Physics Computational Physics |
| url | https://arxiv.org/abs/2308.00670 |