生物启发的自主无人机群中的聚集相变和威胁响应
Flocking phase transition and threat responses in bio-inspired autonomous drone swarms
AI总结：
研究受动物群体启发的自主无人机群，提出3D聚集算法，通过调整交互增益绘制相图，实验表明该算法能使群体对外部干扰更敏感，面对入侵者可快速集体转向等，简单增益调制可调节稳定性等。
摘要：
受动物群体启发的集体运动为自主空中群体提供了强大的设计原则。我们提出了一种受生物启发的3D聚集算法，其中每个无人机仅与最少的有影响力的邻居进行交互，仅依靠局部对齐和吸引线索。通过系统地调整这两个交互增益，我们绘制了一个相图，揭示了群体和鱼群之间的急剧转变，以及一个敏感性、极化波动和重组能力达到峰值的临界区域。使用十架无人机的户外实验，结合使用校准飞行动力学模型的模拟，表明在这个转变附近操作可增强对外部干扰的响应能力。当面对入侵者时，群体执行快速集体转弯、瞬态扩展，并在几秒钟内可靠地恢复高对齐。这些结果表明，最小的局部交互规则足以产生多个集体阶段，并且简单的增益调制提供了一种有效的机制来调整无人机群的稳定性、灵活性和恢复力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
