Article(id=1284878743709651672, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_8V6iIiWx, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1197, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268564393, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268564392, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268564392, creator=admin, updateTime=1784268564392, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', 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IDQN)的多无人机协同灭火路径规划算法。首先,建立基于相同时间间隔的多无人机单目标决策模型;其次,通过优先经验回放机制提高算法训练稳定性,采用26向三维动作空间增强机动性,并构建包含方向引导、步数惩罚、到达奖励和碰撞惩罚的多目标复合奖励函数;最后,基于任务优先级,采用层次化加权法在MATLAB平台进行仿真训练,实现多无人机协同灭火路径规划。仿真结果表明:IDQN算法多无人机协同灭火方案任务完成时长相比单无人机作业缩短58.0%,相比粒子群优化(Particle Swarm Optimization, PSO)算法缩短27.8%,显著提升了城市火灾救援的响应速度和资源利用率。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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基于改进DQN算法的多无人机城市火灾协同灭火研究
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覃睿 1 , 张晓玲 1
安全与环境学报 | 安全工程 2026,26(6):
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安全与环境学报 |安全工程 2026 , 26 (6) :
基于改进DQN算法的多无人机城市火灾协同灭火研究
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覃睿1 , 张晓玲1
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  • 1.中国民航大学交通科学与工程学院
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出版时间: 2026-06-25 doi: 10.13637/j.issn.1009-6094.2025.1197
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针对城市火灾中建筑密集、道路堵塞导致传统消防车救援困难、单无人机灭火能力不足的问题,提出了一种基于改进深度Q网络(Improved Deep Q-Network, IDQN)的多无人机协同灭火路径规划算法。首先,建立基于相同时间间隔的多无人机单目标决策模型;其次,通过优先经验回放机制提高算法训练稳定性,采用26向三维动作空间增强机动性,并构建包含方向引导、步数惩罚、到达奖励和碰撞惩罚的多目标复合奖励函数;最后,基于任务优先级,采用层次化加权法在MATLAB平台进行仿真训练,实现多无人机协同灭火路径规划。仿真结果表明:IDQN算法多无人机协同灭火方案任务完成时长相比单无人机作业缩短58.0%,相比粒子群优化(Particle Swarm Optimization, PSO)算法缩短27.8%,显著提升了城市火灾救援的响应速度和资源利用率。
安全工程  /  深度强化学习算法  /  多无人机协同  /  城市火灾  /  协同灭火  /  路径规划
覃睿, 张晓玲. 基于改进DQN算法的多无人机城市火灾协同灭火研究. 安全与环境学报, 2026 , 26 (6) . DOI: 10.13637/j.issn.1009-6094.2025.1197

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2026年第26卷第6期
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doi: 10.13637/j.issn.1009-6094.2025.1197
  • 首发时间:2026-07-17
  • 出版时间:2026-06-25
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    1.中国民航大学交通科学与工程学院
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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