FireHunter: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling

Abstract

Multi-robot systems are adept at handling complex tasks in large-scale, dynamic, and cold-start scenarios such as wildfire control. This paper introduces FireHunter to tackle the challenge of coordinating fire monitoring and suppression tasks simultaneously in unpredictable environments. FireHunter utilizes a confidence-aware assessment method to identify optimal locations and a priority graph-based algorithm to coordinate robots efficiently. It effectively manages the dynamic planning inclinations for sensing and operational tasks, ensuring real-time information collection and timely environmental intervention. Experimental results from simulation show that FireHunter reduces fire expansion ratio by 59% compared to state-of-the-art solutions.

Fan DANG

Assistant Professor

My research interests include industrial intelligence and edge computing.