Multi-UAV fire rescue path planning based on Monte Carlo city modeling

To address the issues of insufficient universality in algorithm validation and slow convergence in current path planning for multiple unmanned aerial vehicles (UAVs) involved in firefighting operations in urban fire scenarios, this study proposes a path planning and simulation method that integrates a Monte Carlo simulation–based urban model with an improved A* algorithm. A generalized urban model is constructed to reduce the path length and operational time of multi-UAV firefighting missions. The spatial distribution patterns of urban clusters are analyzed, and the A* algorithm is adopted as the path search method, with the shortest time and path length defined as the objective functions. In addition, a time-window optimization mechanism is incorporated to prevent collisions. Simulations and comparative analyses are conducted under three urban scenarios: large metropolitan cities, dual-core cities, and small-scale cities. The simulation results demonstrate the efficiency of the improved A* algorithm and enhance the operational performance of multi-UAV systems in urban fire scenarios. According to the numerical results, the improved A* algorithm achieves the fastest convergence across the three urban scenarios, reducing convergence time by up to 6.89%, 5.78%, and 1.27%, respectively, compared with other algorithms.