通过自适应密度控制，让机器人集群高效安全通过狭窄虚拟通道。
Navigating Robot Swarm Through a Virtual Tube with Flow-Adaptive Distribution Control
- 结合改进势场法与密度反馈，动态调节集群分布。
- 在狭窄通道中实现零碰撞且密度稳定，通过率显著提升。
- 适合需高密度协同的智能交通、搜救等场景。
随着机器人集群技术快速发展及其多样化应用，如何在复杂环境中导航成为关键研究方向。为确保安全并避免障碍物碰撞，虚拟通道被引入以定义安全通行区域。然而，现有控制方法在狭窄通道中易出现拥堵，导致吞吐量低。为此，本文提出一种新方法：结合改进的人工势场（APF）用于集群导航，以及密度反馈控制实现分布调节；生成全局速度场，在保障无碰撞导航的同时，实现局部输入-状态稳定性（LISS）的密度跟踪。数值仿真与真实应用场景验证了该方法在狭窄虚拟通道中导航机器人集群的有效性与优势。
原文摘要 · Abstract (English)
With the rapid development of robot swarm technology and its diverse applications, navigating robot swarms through complex environments has emerged as a critical research direction. To ensure safe navigation and avoid potential collisions with obstacles, the concept of virtual tubes has been introduced to define safe and navigable regions. However, current control methods in virtual tubes face the congestion issues, particularly in narrow ones with low throughput. To address these challenges, we first propose a novel control method that combines a modified artificial potential field (APF) for swarm navigation and density feedback control for distribution regulation. Then we generate a global velocity field that not only ensures collision-free navigation but also achieves locally input-to-state stability (LISS) for density tracking. Finally, numerical simulations and realistic applications validate the effectiveness and advantages of the proposed method in navigating robot swarms through narrow virtual tubes.
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