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Adaptive leadership overcomes persistence–responsivity trade-off in flocking
Author(s) -
B. Balázs,
Gábor Vásárhelyi,
Tamás Vicsek
Publication year - 2020
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2019.0853
Subject(s) - flocking (texture) , responsivity , swarm behaviour , collective motion , collective behavior , persistence (discontinuity) , computer science , perception , cognitive psychology , communication , artificial intelligence , physics , psychology , engineering , neuroscience , telecommunications , geotechnical engineering , quantum mechanics , detector , sociology , anthropology
The living world is full of cohesive collectives that have evolved to move together with high efficiency. Schools of fish or flocks of birds maintain their global direction despite significant noise perturbing the individuals, yet they are capable of performing abrupt collective turns when relevant agitation alters the state of a few members. Ruling local fluctuations out of global movement leads to persistence and requires overdamped interaction dynamics, while propagating swift turns throughout the group leads to responsivity and requires underdamped interaction dynamics. In this paper we show a way to avoid this conflict by introducing a time-dependent leadership hierarchy that adapts locally to will : agents’ intention of changing direction. Integrating our new concept of will -based inter-agent behaviour highly enhances the responsivity of standard collective motion models, thus enables breaking out of their former limit, the persistence-responsivity trade-off. We also show that the increased responsivity to environmental cues scales well with growing flock size. Our solution relies on active communication or advanced cognition for the perception of will . The incorporation of these into collective motion is a plausible hypothesis in higher order species, while it is a realizable feature for artificial robots, as demonstrated by our swarm of 52 drones.

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