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

2025-11-17

AI Summary

In high-speed navigation of multi-robot systems within narrow, dynamic environments, frequent reassignment of dynamic priority among heterogeneous agents compromises the simultaneous achievement of collision-avoidance fairness and agility. Method: This paper proposes a distributed cooperative collision-avoidance framework integrating natural-language communication with an enhanced artificial potential field (APF) method. It introduces a novel “roundabout-inspired” potential field structure and enables real-time priority negotiation via natural-language instructions, achieving decentralized, low-latency conflict resolution. Contribution/Results: The approach breaks from conventional static negotiation paradigms, ensuring both safety margin and task fairness under dynamic role switching. Experiments demonstrate over 3.5× improvement in task completion speed and more than 70% reduction in total mission time compared to baseline methods, validating its efficiency and robustness in complex, dynamic environments.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Agent Communication
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Enabling safe high-speed navigation in crowded constrained environments
• Managing dynamic priority changes among heterogeneous multi-robot systems
• Balancing collision safety with mission agility in urgent response scenarios

Innovation

• Natural language communication for robot coordination
• Novel artificial potential field algorithm
• Automatic roundabout effect for conflict resolution