发现蚂蚁、物理系统与机器人集群共享同一随机行为机制。
A Unified Stochastic Mechanism Underlying Collective Behavior in Ants, Physical Systems, and Robotic Swarms
- 通过最大化不同能量约束下的熵，统一解释生物与物理系统的随机行为。
- 机器人集群按此机制可实现无中心化协作，具备类相变行为特征。
- 为智能群集机器人设计提供可扩展的鲁棒性原则，适合工程应用。
蚁群等生物集群通过去中心化、随机的个体行为达成集体目标。类似地，由气体、液体和固体组成的物理系统也表现出受熵最大化支配的随机粒子运动，但无法实现集体目标。尽管存在这种类比，目前尚无统一框架解释生物与物理系统中的随机行为。本文通过对细叶蚁（Formica polyctena）的实证研究，揭示两类系统共享一种统计机制：在不同能量函数约束下进行最大化。我们进一步证明，遵循该原则的机器人集群能实现可扩展的去中心化协作，展现出类相变行为，且个体计算开销极低。这些发现建立了一个连接生物、物理与机器人集群的统一随机模型，为设计鲁棒、智能的群体机器人提供了可扩展的原则。
原文摘要 · Abstract (English)
Biological swarms, such as ant colonies, achieve collective goals through decentralized and stochastic individual behaviors. Similarly, physical systems composed of gases, liquids, and solids exhibit random particle motion governed by entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain the stochastic behavior in both biological and physical systems. Here, we present empirical evidence from \textit{Formica polyctena} ants that reveals a shared statistical mechanism underlying both systems: maximization under different energy function constraints. We further demonstrate that robotic swarms governed by this principle can exhibit scalable, decentralized cooperation, mimicking physical phase-like behaviors with minimal individual computation. These findings established a unified stochastic model linking biological, physical, and robotic swarms, offering a scalable principle for designing robust and intelligent swarm robotics.
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