Flocking phase transition and threat responses in bio-inspired autonomous drone swarms

TL;DR

This paper introduces a bio-inspired 3D flocking algorithm for drone swarms, demonstrating phase transitions between swarming and schooling, and showing that operating near critical points enhances responsiveness and threat response capabilities.

Contribution

The study develops a minimal local-interaction flocking model with tunable gains, revealing phase transitions and critical behavior, validated through outdoor drone experiments and simulations.

Findings

Sharp phase transitions between swarming and schooling behaviors.

Operating near critical points improves responsiveness to disturbances.

Minimal local rules suffice for complex collective behaviors.

Abstract

Collective motion inspired by animal groups offers powerful design principles for autonomous aerial swarms. We present a bio-inspired 3D flocking algorithm in which each drone interacts only with a minimal set of influential neighbors, relying solely on local alignment and attraction cues. By systematically tuning these two interaction gains, we map a phase diagram revealing sharp transitions between swarming and schooling, as well as a critical region where susceptibility, polarization fluctuations, and reorganization capacity peak. Outdoor experiments with a swarm of ten drones, combined with simulations using a calibrated flight-dynamics model, show that operating near this transition enhances responsiveness to external disturbances. When confronted with an intruder, the swarm performs rapid collective turns, transient expansions, and reliably recovers high alignment within seconds. These results demonstrate that minimal local-interaction rules are sufficient to generate multiple collective phases and that simple gain modulation offers an efficient mechanism to adjust stability, flexibility, and resilience in drone swarms.