目的随着虚拟现实技术的发展,在虚拟场景中,基于多智能体的逃生路径规划已成为关键技术之一。与传统的火灾演习相比,采用基于虚拟现实的方法完成火灾逃生演练具有诸多优势,如成本低、代价小、可靠性高等,但仍有一定的局限性,为此,提出...目的随着虚拟现实技术的发展,在虚拟场景中,基于多智能体的逃生路径规划已成为关键技术之一。与传统的火灾演习相比,采用基于虚拟现实的方法完成火灾逃生演练具有诸多优势,如成本低、代价小、可靠性高等,但仍有一定的局限性,为此,提出一种改进的双层深度Q网络(deep Q network,DQN)架构的路径规划算法。方法基于两个结构相同的双Q网络,优化了经验池的生成方法和探索策略,并在奖励中增加火灾这样的环境因素对智能体的影响。同时,为了提高疏散的安全性和效率,提出了一种基于改进的K-medoids算法的多智能体分组策略方法。结果相关实验表明提出的改进的双层深度Q网络架构收敛速度更快,学习更加稳定,模型性能得到有效提升。综合考虑火灾场景下智能体的疏散效率和疏散安全性,使用指标平均健康疏散值(average health evacuation value,AHEP)评估疏散效果,相较于传统的路径规划方法A-STAR(a star search algorithm)和DIJKSTRA(Dijkstra’s algorithm)分别提高了84%和104%;与基于火灾场景改进的扩展A-STAR和Dijkstra-ACO(Dijkstra and ant colony optimization)混合算法比较,分别提高了30%和21%;与考虑火灾影响的DQN算法相比,提高了20%,疏散效率和安全性都得到提高,规划的路径疏散效果更好。通过比较不同分组模式下的疏散效果,验证了对多智能体合适分组可以提高智能体疏散效率。结论提出的算法优于目前大多数常用的方法,显著提高了疏散的效率和安全性。展开更多
There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key proble...There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.展开更多
文摘目的随着虚拟现实技术的发展,在虚拟场景中,基于多智能体的逃生路径规划已成为关键技术之一。与传统的火灾演习相比,采用基于虚拟现实的方法完成火灾逃生演练具有诸多优势,如成本低、代价小、可靠性高等,但仍有一定的局限性,为此,提出一种改进的双层深度Q网络(deep Q network,DQN)架构的路径规划算法。方法基于两个结构相同的双Q网络,优化了经验池的生成方法和探索策略,并在奖励中增加火灾这样的环境因素对智能体的影响。同时,为了提高疏散的安全性和效率,提出了一种基于改进的K-medoids算法的多智能体分组策略方法。结果相关实验表明提出的改进的双层深度Q网络架构收敛速度更快,学习更加稳定,模型性能得到有效提升。综合考虑火灾场景下智能体的疏散效率和疏散安全性,使用指标平均健康疏散值(average health evacuation value,AHEP)评估疏散效果,相较于传统的路径规划方法A-STAR(a star search algorithm)和DIJKSTRA(Dijkstra’s algorithm)分别提高了84%和104%;与基于火灾场景改进的扩展A-STAR和Dijkstra-ACO(Dijkstra and ant colony optimization)混合算法比较,分别提高了30%和21%;与考虑火灾影响的DQN算法相比,提高了20%,疏散效率和安全性都得到提高,规划的路径疏散效果更好。通过比较不同分组模式下的疏散效果,验证了对多智能体合适分组可以提高智能体疏散效率。结论提出的算法优于目前大多数常用的方法,显著提高了疏散的效率和安全性。
基金Project supported by the Key Research Projects of the Central University of Basic Scientific Research Funds for Cross Cooperation,China(No.201510-02)the Research Fund for the Doctoral Program of Higher Education,China(No.2013007211-0035)the Key Project in Science and Technology of Jilin Province,China(No.20140204088GX)
文摘There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.
