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Forest fire safety detection and personnel evacuation based on collaborative MUAVs
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Peng GENG1, Haojie YANG1, Fanglin XUE1, Yan LIU2, **
China Safety Science Journal | 2025, 35(4) : 43 - 50
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China Safety Science Journal | 2025, 35(4): 43-50
• Safety engineering technology •
Forest fire safety detection and personnel evacuation based on collaborative MUAVs
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Peng GENG1, Haojie YANG1, Fanglin XUE1, Yan LIU2, **
Affiliations
  • 1 School of Communication and Artificial Intelligence，School of Integrated Circuits，Nanjing Institute of Technology，Nanjing Jiangsu 211167，China
  • 2 School of Mathematics and Physics，Nanjing Institute of Technology，Nanjing Jiangsu 211167，China
Published: 2025-04-28 doi: 10.16265/j.cnki.issn1003-3033.2025.04.0958
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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%.

forest fire  /  multiple unmanned aerial vehicles(MUAVs)  /  personnel evacuation  /  fire detection  /  improved ant colony algorithm  /  A* algorithm
Peng GENG, Haojie YANG, Fanglin XUE, Yan LIU. Forest fire safety detection and personnel evacuation based on collaborative MUAVs[J]. China Safety Science Journal, 2025 , 35 (4) : 43 -50 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.0958
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.04.0958
  • Receive Date：2024-12-10
  • Online Date：2025-07-05
  • Published：2025-04-28
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  • Received：2024-12-10
  • Revised：2025-02-15
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Affiliations
    1 School of Communication and Artificial Intelligence，School of Integrated Circuits，Nanjing Institute of Technology，Nanjing Jiangsu 211167，China
    2 School of Mathematics and Physics，Nanjing Institute of Technology，Nanjing Jiangsu 211167，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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