Research on Risk Evaluation and Dynamic Escape Path Planning Algorithm Based on Real-Time Spread of Ship Comprehensive Fire

被引:13
|
作者
Ji, Jian [1 ]
Ma, Zhihao [2 ]
He, Jiajun [2 ]
Xu, Yingjun [1 ]
Liu, Zhiqiang [2 ]
机构
[1] Zhejiang Marine Dev Res Inst, Zhoushan 316021, Peoples R China
[2] Jiangsu Univ Sci & Technol, Sch Mech Engn, Zhenjiang 212000, Jiangsu, Peoples R China
关键词
real-time fire situation; fuzzy neural network; escape route; A(star) algorithm;
D O I
10.3390/jmse8080602
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
As an independent building entity on the sea, the ship has a large number of internal electrical equipment and a compact space structure, which is prone to fire. This paper proposes a key technology of virtual dynamic escape of ships based on the fire spread prediction model for research. Taking the 63,500 DWT(Dead Weight Tonnage) tanker cabin as a research entity, the mathematical and physical models of ship fire simulation are established. Through the graphical analysis of the experimental data of the fire spread simulation, the temperature, CO concentration, and smoke concentration change rules under different working conditions at the fixed detection point position are obtained. Then, based on temperature, CO concentration and smoke concentration three impact factors, set up a comprehensive fire real-time situational risk evaluation index system. Using the MATLAB software, based on the principle of the fuzzy neural network fire ship's integrated real-time situational risk evaluation model structure design and simulation test, obtained the corresponding training to comprehensive risk evaluation model of the network. Generate navigation grid according to the law of fire sprawl, and plan escape path. The traditional A(star) algorithm is improved, and an example is used to prove that the path-finding result after the improved algorithm is shorter than the path found by the traditional algorithm, which meets the path-finding requirements in a three-dimensional environment.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] An efficient dynamic system for real-time robot-path planning
    Willms, Allan R.
    Yang, Simon X.
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2006, 36 (04): : 755 - 766
  • [42] Potential risk ship domain as a danger criterion for real-time ship collision risk evaluation
    Im, Namkyun
    Luong, Tu Nam
    OCEAN ENGINEERING, 2019, 194
  • [43] Improved three-dimensional A* algorithm of real-time path planning based on reinforcement learning
    Ren Z.
    Zhang D.
    Tang S.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2023, 45 (01): : 193 - 201
  • [44] Research on Ship Replenishment Path Planning Based on the Modified Whale Optimization Algorithm
    Chen, Qinghua
    Yao, Gang
    Yang, Lin
    Liu, Tangying
    Sun, Jin
    Cai, Shuxiang
    BIOMIMETICS, 2025, 10 (03)
  • [45] A Real-Time USV Path Planning Algorithm in Unknown Environment Based on Deep Reinforcement Learning
    Zhou, Zhi-Guo
    Zheng, Yi-Peng
    Liu, Kai-Yuan
    He, Xu
    Qu, Chong
    Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2019, 39 : 86 - 92
  • [46] Real-Time UAV Path Planning Based on LSTM Network
    Zhang, Jiandong
    Guo, Yukun
    Zheng, Lihui
    Yang, Qiming
    Shi, Guoqing
    Wu, Yong
    Journal of Systems Engineering and Electronics, 2024, 35 (02) : 374 - 385
  • [47] Real-Time UAV Path Planning Based on LSTM Network
    Zhang, Jiandong
    Guo, Yukun
    Zheng, Lihui
    Yang, Qiming
    Shi, Guoqing
    Wu, Yong
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2024, 35 (02) : 374 - 385
  • [48] Real-time UAV path planning based on LSTM network
    ZHANG Jiandong
    GUO Yukun
    ZHENG Lihui
    YANG Qiming
    SHI Guoqing
    WU Yong
    JournalofSystemsEngineeringandElectronics, 2024, 35 (02) : 374 - 385
  • [49] Real-time Model Based Path Planning for Wheeled Vehicles
    Jordan, Julian
    Zell, Andreas
    2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2019, : 5787 - 5792
  • [50] A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance
    Chen, Pengzhan
    Pei, Jiean
    Lu, Weiqing
    Li, Mingzhen
    NEUROCOMPUTING, 2022, 497 : 64 - 75