Flocking phase transition and threat responses in bio-inspired autonomous drone swarms
For autonomous drone swarm control, this work demonstrates that minimal local interaction rules can generate multiple collective phases and that gain modulation provides an efficient mechanism to adjust stability and resilience.
This paper presents a bio-inspired 3D flocking algorithm for drone swarms using only local alignment and attraction cues, and maps a phase diagram revealing transitions between swarming and schooling. Outdoor experiments with ten drones show that operating near the critical transition enhances responsiveness to disturbances, enabling rapid collective turns and recovery within seconds.
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.