通过调控无人机群的局部交互规则，实现自适应响应外部威胁的群体行为。
Flocking phase transition and threat responses in bio-inspired autonomous drone swarms
- 基于局部对齐与吸引机制，仅与少数邻居互动
- 在相变临界区操作时，响应速度提升数倍
- 适合需要快速应变的无人机集群任务
受动物群体运动启发，我们提出一种3D蜂群算法，每架无人机仅与少数关键邻近无人机交互，依赖局部对齐和吸引信号。通过系统调节这两类交互增益，绘制出相图，揭示了蜂群与鱼群之间的清晰相变，以及一个临界区域，在该区域扰动敏感性、极化波动和重组能力均达峰值。十架无人机的户外实验结合校准的飞行动力学模拟显示，处于此相变附近时，群体对外部干扰响应更灵敏。遭遇入侵者时，群体会迅速集体转向、短暂扩张，并在数秒内恢复高对齐状态。结果表明，仅需最小局部交互规则即可生成多种集体相态，且简单增益调节可高效调控无人机集群的稳定性、灵活性与鲁棒性。
原文摘要 · Abstract (English)
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.
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