提出一种无需碰撞规避与刚性结构的微型多机器人集体行为模型。
A Minimal Model for Emergent Collective Behaviors in Autonomous Robotic Multi-Agent Systems
- 基于相对位置、速度和局部密度调控，仅用两个参数实现灵活群体运动。
- 能自然生成无碰撞、自适应的群集与编队行为，支持能量感知切换。
- 适合无人机集群等实际多机器人系统，具生物启发性与工程实用性。
自然界中，群体行为如蜂群与迁徙鸟群源于简单的去中心化交互。现有模型如Vicsek和Cucker-Smale缺乏碰撞规避能力，而Olfati-Saber模型强制刚性构型，限制了在集群机器人中的应用。本文提出一种最小但表达力强的模型，通过相对位置、速度和局部密度，并结合两个可调参数——空间偏移量与动能偏移量——来控制智能体动态。该模型实现了空间上灵活、无碰撞的群体行为，体现自然化的集体动力学特征。进一步，将框架扩展至认知自主系统，通过自适应调节控制参数，实现能量感知的群集与编队之间的相变。这一受认知启发的方法为多机器人系统，尤其是自主飞行集群，提供了稳健的应用基础。
原文摘要 · Abstract (English)
Collective behaviors such as swarming and flocking emerge from simple, decentralized interactions in biological systems. Existing models, such as Vicsek and Cucker-Smale, lack collision avoidance, whereas the Olfati-Saber model imposes rigid formations, limiting their applicability in swarm robotics. To address these limitations, this paper proposes a minimal yet expressive model that governs agent dynamics using relative positions, velocities, and local density, modulated by two tunable parameters: the spatial offset and kinetic offset. The model achieves spatially flexible, collision-free behaviors that reflect naturalistic group dynamics. Furthermore, we extend the framework to cognitive autonomous systems, enabling energy-aware phase transitions between swarming and flocking through adaptive control parameter tuning. This cognitively inspired approach offers a robust foundation for real-world applications in multi-robot systems, particularly autonomous aerial swarms.
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