A Two-Stage Mission Planning Method for UAV-Based Fire Suppression in High-Rise Buildings

Abstract

High-rise building fires pose substantial challenges to conventional firefighting operations due to restricted rescue space and the difficulty of delivering suppression resources rapidly. To improve response efficiency, this study proposes a two-stage mission planning framework for multi-station UAV-based firefighting. The proposed methodology simultaneously accounts for environmental wind, building obstacles, fire evolution, and UAV payload constraints. In the first stage, an improved particle swarm optimization (PSO) algorithm is employed to generate time-optimal flight paths satisfying both spatial obstacle-avoidance and wind-field constraints. In the second stage, based on the actual flight times derived from the first stage, the multi-UAV resource scheduling problem is formulated as a mixed-integer linear programming (MILP) model to minimize the total fire suppression mission duration. Additionally, an isochrone-based firefighting coverage circle is introduced to optimize the layout of additional fire stations. Simulation results indicate that while optimized paths remain geometrically similar under varying wind conditions, wind-induced flight time variations significantly affect UAV arrival sequences and flight times. In the scheduling stage, differences in station layouts and fire scales alter projectile release timing; under unfavorable conditions, such temporal differences can increase the total mission duration by more than 28%. Notably, the optimized addition of fire stations effectively enhances response redundancy in high-rise clusters, reducing fire suppression time in adjacent scenarios by approximately 50%. The proposed method provides theoretical support and methodological guidance for cooperative UAV firefighting and emergency resource optimization in urban environments.