关键词: 森林火灾, 多无人机(MUAVs), 人员疏散, 火点探测, 改进蚁群算法, A^*算法

Abstract:

To address the current challenges of lacking unmanned detection systems amid frequent forest fires and inefficient personnel evacuation during uncontrolled fire scenarios, this article proposes a forest fire safety detection method based on collaborativeMUAVs and an optimized shelter location strategy. A dynamic forest fire spread model coupled with multiple influencing factors is developed on the NetLogo platform. MUAVscollaborative search mechanism, grounded in an improved ant colony algorithm, is enhanced by introducing attractive pheromones (guiding searches toward fire clusters) and repellent pheromones (avoiding redundant paths), thereby optimizing the transfer probability of unmanned aerial vehicle (UAV) flight directions. Additionally, a flight model incorporating obstacle avoidance and water-carrying capacity-speed constraints was established. A dynamic evacuation simulation environment was constructed using geographic information system (GIS) data from Rhodes Island, Greece. Experimental results demonstrate that the improved ant colony algorithm reduces convergence time by 15% and 14% under 50% and 60% tree density scenarios, respectively, while search coverage increases by 35.02% and 32.16%. Furthermore, optimized shelter placement combined with the A^* algorithm-based evacuation strategy reduces the overall mortality rate by 2.525%.

Key words: forest fire, multiple unmanned aerial vehicles(MUAVs), personnel evacuation, fire detection, improved ant colony algorithm, A^* algorithm