Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to the swarm. In the hope of addressing such vulnerabilities, developing bio-inspired decentralized swarms has been a major focus in recent decades. Yet, creating robots that move efficiently together using only local sensory information remains an extraordinary challenge. In this work, we present a decentralized, purely vision-based swarm of terrestrial robots. Within this novel framework robots achieve collisionless, polarized motion exclusively through minimal visual interactions, computing everything on board based on their individual camera streams, making central processing or direct communication obsolete. With agent-based simulations, we further show that using this model, even with a strictly limited field of view and within confined spaces, ordered group motion can emerge, while also highlighting key limitations. Our results offer a multitude of practical applications from hybrid societies coordinating collective movement without any common communication protocol, to advanced, decentralized vision-based robot swarms capable of diverse tasks in ever-changing environments.

翻译：受动物群体启发的集体运动为机器人集群带来了固有优势，如增强的感知能力和运行效率。然而，尽管动物仅依靠局部感知进行群体移动，机器人通常遵循中央控制或使用直接通信，这给集群引入了系统性弱点。为解决此类脆弱性，开发生物启发的去中心化集群已成为近几十年来的研究重点。然而，创建仅依靠局部感官信息就能高效协同移动的机器人仍然是一项非凡挑战。本研究提出了一种去中心化、纯视觉驱动的陆地机器人集群。在此新颖框架中，机器人仅通过最小化的视觉交互实现无碰撞的极化运动，所有计算均基于个体摄像头流在机载设备上完成，从而无需中央处理或直接通信。通过基于智能体的仿真，我们进一步证明：即使采用严格受限的视野范围并在有限空间内，该模型仍能涌现出有序的群体运动，同时也揭示了关键局限性。我们的研究成果具有多重实际应用价值——从无需通用通信协议即可协调集体运动的混合社会系统，到能够在动态环境中执行多样化任务的先进去中心化视觉机器人集群。