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Adaptive leadership overcomes persistence-responsivity trade-off in flocking.

Boldizsár Balázs1, Gábor Vásárhelyi2, Tamás Vicsek1,2

  • 1Eötvös Loránd University, Budapest, Hungary.

Journal of the Royal Society, Interface
|June 11, 2020
PubMed
Summary

This study introduces a novel leadership hierarchy that adapts to agents' intention, enhancing collective movement efficiency. This breakthrough overcomes the persistence-responsivity trade-off in flocking behavior for both natural and artificial systems.

Keywords:
agent-based modellingcollective behaviourcollective motionhierarchyresponse theoryself-organizing drones

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Area of Science:

  • Collective Behavior
  • Robotics
  • Complex Systems

Background:

  • Cohesive collectives like fish schools and bird flocks exhibit efficient group movement.
  • These groups maintain direction amidst noise but can perform abrupt turns.
  • Standard models face a trade-off between persistence (stability) and responsivity (agility).

Purpose of the Study:

  • To resolve the persistence-responsivity trade-off in collective motion models.
  • To introduce a mechanism for enhanced group agility without sacrificing stability.
  • To demonstrate the applicability of the proposed model in biological and artificial systems.

Main Methods:

  • Introduction of a time-dependent leadership hierarchy adapting to agent 'will' (intention to change direction).
  • Integration of 'will'-based inter-agent behavior into standard collective motion models.
  • Experimental validation using a swarm of 52 drones.

Main Results:

  • Significant enhancement of collective motion model responsivity.
  • Breaking the inherent persistence-responsivity limitation of traditional models.
  • Demonstrated scalability of enhanced responsivity with increasing flock size.
  • Successful implementation in a physical system (drone swarm).

Conclusions:

  • A 'will'-based adaptive leadership hierarchy effectively balances persistence and responsivity.
  • The proposed mechanism offers a viable solution for improving collective movement in dynamic environments.
  • The findings have implications for understanding animal behavior and designing advanced robotic swarms.