Forest fire safety detection and personnel evacuation based on collaborative MUAVs

doi:10.16265/j.cnki.issn1003-3033.2025.04.0958

Abstract

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

CLC Number:

X928.7火灾与爆炸事故

GENG Peng, YANG Haojie, XUE Fanglin, LIU Yan. Forest fire safety detection and personnel evacuation based on collaborative MUAVs[J]. China Safety Science Journal, 2025, 35(4): 43-50.

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参数名称	参数规格
吸引信息素蒸发系数	0.7
排斥信息素蒸发系数	0.5
吸引信息素增量	0.1
排斥信息素增量	0.2
随机行走算法转移概率	各方向概率相等
初始火点数量	3
每步火蔓延到相邻树木的概率	0.2
UAV数量	40
随机数量	5
初始水负荷	10