VG-Swarm：一种用于无人机集群行为涌现的基于视觉的基因调控网络
VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence
AI总结：
针对无人机集群围捕依赖集中控制与通信的问题，提出纯视觉结合GRN的分布式控制方法，并用FSM实现集体搜索与围捕，经仿真和真实实验验证有效。
摘要：
无人机（UAVs）动态围捕是一个新兴且具有巨大潜力的领域。研究人员经常从生物系统中获取灵感，既包括鱼群、鸟群等宏观世界，也包括基因调控网络（GRN）等微观世界。然而，大多数集群控制算法依赖集中式控制、全局信息获取以及相邻智能体之间的通信。在本工作中，我们提出一种完全基于视觉和GRN、无需任何直接通信的分布式集群控制方法，其中例如无人机等集群智能体能够完全依靠其安装的全向视觉传感器，生成围捕模式以包围一架逃逸的目标无人机。文中还设计了用于描述每架无人机行为模型的有限状态机（FSM），使无人机集群能够以一体化方式集体完成对目标的搜索与围捕。我们在多种仿真和真实世界实验中验证了所提方法的有效性和效率。
英文摘要：
Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments.
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。
