Multi-robot systems are increasingly being used for critical applications such as rescuing injured people, delivering food and medicines, and monitoring key areas. These applications usually involve navigating at high speeds through constrained spaces such as small gaps. Navigating such constrained spaces becomes particularly challenging when the space is crowded with multiple heterogeneous agents all of which have urgent priorities. What makes the problem even harder is that during an active response situation, roles and priorities can quickly change on a dime without informing the other agents. In order to complete missions in such environments, robots must not only be safe, but also agile, able to dodge and change course at a moment's notice. In this paper, we propose FACA, a fair and agile collision avoidance approach where robots coordinate their tasks by talking to each other via natural language (just as people do). In FACA, robots balance safety with agility via a novel artificial potential field algorithm that creates an automatic ``roundabout'' effect whenever a conflict arises. Our experiments show that FACA achieves a improvement in efficiency, completing missions more than 3.5X faster than baselines with a time reduction of over 70% while maintaining robust safety margins.

翻译：多机器人系统正日益广泛地应用于关键任务，如救援伤员、运送食品与药品、以及监控关键区域。这些应用通常需要高速穿越受限空间，例如狭窄通道。当此类受限空间被多个具有紧急优先级的异构智能体密集占据时，导航变得尤为困难。更复杂的是，在动态响应场景中，角色与优先级可能瞬息万变，且无需通知其他智能体。为在此类环境中完成任务，机器人不仅需确保安全，还必须具备敏捷性，能够即时规避与改变航向。本文提出FACA，一种公平且敏捷的碰撞规避方法，其中机器人通过自然语言相互协调任务（如同人类交流）。在FACA中，机器人通过一种新颖的人工势场算法平衡安全性与敏捷性，该算法在冲突发生时自动产生“环岛”效应。实验表明，FACA在效率上取得显著提升，任务完成速度较基线方法加快超过3.5倍，时间减少70%以上，同时保持稳健的安全裕度。