FACA：约束环境下具备动态优先级的公平敏捷多机器人避障方法
FACA: Fair and Agile Multi-Robot Collision Avoidance in Constrained Environments with Dynamic Priorities
AI总结：
针对受限动态环境下多机器人避障时优先级易突变、需兼顾安全与敏捷性的问题，提出基于自然语言交互与新型人工势场的FACA方法，相比基线提速超3.5倍、耗时降70%以上且保障安全。
摘要：
多机器人系统正越来越多地应用于营救伤员、配送食品与药品、监控关键区域等关键任务场景。这类应用通常需要机器人在狭窄间隙等受限空间内高速通行。当受限空间内挤满均具备紧急优先级的异构智能体时，这类空间的导航任务会变得极具挑战性。让问题难度进一步提升的是，在主动响应场景中，角色与优先级可能在不告知其他智能体的情况下瞬间发生变化。为了在此类环境中完成任务，机器人不仅要保证安全性，还需具备敏捷性，能够在接到通知的瞬间完成闪避与航向调整。本文提出FACA，一种公平敏捷的避障方法，该方法中机器人可像人类一样通过自然语言交互来协调任务。在FACA中，机器人通过一种新型人工势场算法平衡安全性与敏捷性，该算法会在冲突发生时自动产生“环岛”通行效果。实验表明，FACA实现了效率提升，完成任务的速度是基线方法的3.5倍以上，耗时减少超70%，同时维持了可靠的安全裕度。
英文摘要：
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.
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。
