Our ability to understand and control the emergence of order in swarming systems is a fundamental challenge in contemporary science. The standard Vicsek model (SVM) — a minimal model for swarming systems of self-propelled particles — describes a large population of agents reaching global alignment without the need of central control. Yet, the emergence of order in this model takes time and is not robust to noise. In many real-world scenarios, we need a decentralized protocol to guide a swarming system (e.g., unmanned vehicles or nanorobots) to reach an ordered state in a prompt and noise-robust manner. Here, we find that introducing a simple adaptive rule based on the heading differences of neighboring particles in the Vicsek model can effectively speed up their global alignment, mitigate the disturbance of noise to alignment, and maintain a robust alignment under predation. This simple adaptive model of swarming systems could offer new insights in understanding the prompt and flexible formation of animals and help us design better protocols to achieve fast and robust alignment for multi-agent systems.

中文翻译：

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加速集群系统中秩序的出现

我们理解和控制蜂群系统中秩序出现的能力是当代科学的一项基本挑战。标准 Vicsek 模型 (SVM) - 自推进粒子集群系统的最小模型 - 描述了无需中央控制即可达到全局对齐的大量代理。然而，该模型中顺序的出现需要时间，并且对噪声不鲁棒。在许多现实世界的场景中，我们需要一个去中心化的协议来引导集群系统（例如，无人驾驶车辆或纳米机器人）以快速且抗噪的方式达到有序状态。在这里，我们发现在 Vicsek 模型中引入一个基于相邻粒子航向差异的简单自适应规则可以有效地加速它们的全局对齐，减轻噪声对对齐的干扰，并在捕食下保持稳健的排列。这种简单的蜂群系统自适应模型可以为理解动物的迅速灵活形成提供新的见解，并帮助我们设计更好的协议以实现多智能体系统的快速和稳健的对齐。