FACA: Fair and Agile Multi-Robot Collision Avoidance in Constrained Environments with Dynamic Priorities

TL;DR

FACA is a novel multi-robot collision avoidance method that enables robots to communicate via natural language, balancing safety and agility in constrained environments with dynamic priorities, significantly improving efficiency.

Contribution

The paper introduces FACA, a new approach combining natural language communication and an artificial potential field algorithm for fair, agile, and safe multi-robot navigation in complex spaces.

Findings

FACA completes missions over 3.5 times faster than baseline methods.

Achieves more than 70% reduction in mission time.

Maintains robust safety margins during high-speed navigation.

Abstract

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.