The integration of manned and unmanned aircraft can help improve wildfire response. Wildfire containment failures occur when the resources available to first responders, who execute the initial stages of wildfire management referred to as the initial attack, are ineffective or insufficient. Initial attack surveillance and suppression models have linked action spaces and objectives, making their optimization computationally challenging. The initial attack may be formulated as a multi-agent partially observable Markov decision process (MPOMDP). We divide the initial attack MPOMDP into surveillance and suppression processes with their respective planners operating on different, but constant, time scales. A hierarchical framework iterates between surveillance and suppression planners while also providing collision avoidance. This framework is exemplified by a set of multirotor unmanned aircraft surveying an initial attack fire while a manned helicopter suppresses the fire with a water bucket. Wildfire-specific solver extensions are formulated to reduce the otherwise vast action spaces. The hierarchical framework outperforms firefighting techniques and a myopic baseline by up to 242% for moderate wildfires and 60% for rapid wildfires when simulated in abstracted and actual case studies. We also validate the early dispatching of additional suppression assets using regression models to ensure wildfire containment within thresholds established by wildfire agencies.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hierarchical Framework for Optimizing Wildfire Surveillance and Suppression Using Human-Autonomous Teaming


    Contributors:

    Published in:

    Publication date :

    2024-10-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Autonomous Distributed Wildfire Surveillance using Deep Reinforcement Learning

    Julian, Kyle D. / Kochenderfer, Mykel J. | AIAA | 2018


    Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning

    Julian, Kyle D. / Kochenderfer, Mykel J. | ArXiv | 2018

    Free access


    UAV and UGV Autonomous Cooperation for Wildfire Hotspot Surveillance

    Pasini, Diego / Jiang, Charles / Jolly, Marie-Pierre | IEEE | 2022


    Autonomous Distributed Wildfire Surveillance using Deep Reinforcement Learning (AIAA 2018-1589)

    Julian, Kyle D. / Kochenderfer, Mykel J. | British Library Conference Proceedings | 2018