Adaptive leadership overcomes persistence-responsivity trade-off in flocking

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.