让机器人用语言协作避障，高效安全通过狭窄空间。
FACA: Fair and Agile Multi-Robot Collision Avoidance in Constrained Environments with Dynamic Priorities
- 机器人通过自然语言沟通，动态调整路径
- 比基线快3.5倍以上，时间减少超70%
- 适合高优先级动态变化的救援等场景
多机器人系统正越来越多地应用于救援、送药和关键区域监控等关键任务中。这些任务通常需要在狭窄空间（如小缝隙）以高速行进。当多个异构代理同时具有紧急任务且空间拥挤时，导航难度显著增加。更棘手的是，在应急响应中，角色和优先级可能随时变化，且不通知其他机器人。为在该环境下完成任务，机器人必须既安全又敏捷，能即时规避障碍并改变航向。本文提出FACA，一种公平且敏捷的多机器人避障方法。机器人通过自然语言交流协作，采用新型人工势场算法，在冲突发生时自动形成“环形绕行”效果。实验表明，FACA在保持稳健安全距离的同时，效率提升显著：任务完成速度比基线快3.5倍以上，时间减少超过70%。
原文摘要 · Abstract (English)
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.
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