Three-Dimensional Underwater Path Planning of Submarine Considering the Real Marine Environment

被引:4
|
作者
Fu, Jun [1 ]
Lv, Teng [1 ]
Li, Bao [1 ]
Ning, Zhiwen [1 ]
Chang, Yang [1 ]
机构
[1] Naval Univ Engn, Sch Elect Engn, Nav Engn Teaching & Res Off, Wuhan 430033, Peoples R China
关键词
Underwater vehicles; Path planning; Heuristic algorithms; Convergence; Acoustics; Planning; Genetic algorithms; Submarine; complex marine environment; three-dimensional path planning; artificial potential field (APF); ant colony optimization (ACO); POTENTIAL-FIELD; VEHICLES; ALGORITHM;
D O I
10.1109/ACCESS.2022.3164175
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The submarine is an underwater ship that can perform a variety of combat missions in a complex marine environment. Path planning of submarines has always been the focus of military marine engineering research. In most practical applications, there are numerous marine physical phenomena in the marine environment, such as pycnocline, density fronts, mesoscale eddy, etc., which have an important impact on the navigation of submarines. First, the artificial potential field heuristic factor is introduced into the ant colony algorithm to improve its convergence speed, and the artificial potential field ant colony optimization (APF-ACO) is obtained. In addition, this article uses the unit-body to reflect the regional physical elements and quantifies the physical marine phenomenon in the form of the cost function, which is used to solve the problem of submarine path planning in the complex marine environment. In this article, the algorithm is tested in a real marine data environment. The experimental results show that the algorithm can realize the utilization of the ocean sound speed environment, ocean density environment and ocean current environment, and obtain a path more suitable for submarine underwater navigation.
引用
收藏
页码:37016 / 37029
页数:14
相关论文
共 50 条
  • [21] The Method Based on Dijkstra of Three-dimensional Path Planning
    Zhang, Hongxia
    Cheng, Zihui
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 1698 - 1701
  • [22] Three-Dimensional Path-Planning for a Communications and Navigation Aid Working Cooperatively with Autonomous Underwater Vehicles
    Seto, Mae L.
    Hudson, Jonathan A.
    Pan, Yajun
    AUTONOMOUS AND INTELLIGENT SYSTEMS, 2011, 6752 : 51 - 62
  • [23] Adaptive Bi-Directional RRT Algorithm for Three-Dimensional Path Planning of Unmanned Aerial Vehicles in Complex Environments
    Li, Nan
    Han, Sang Ik
    IEEE ACCESS, 2025, 13 : 23748 - 23767
  • [24] Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication
    Tang, Yeshuang
    Chen, Haoxian
    Ma, Zhaoyong
    Jin, Zichen
    Yin, Huili
    IEEE ACCESS, 2024, 12 : 79238 - 79250
  • [25] Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system
    Wang Honglun
    Lyu Wentao
    Peng, Yao
    Xiao, Liang
    Chang, Liu
    CHINESE JOURNAL OF AERONAUTICS, 2015, 28 (01) : 229 - 239
  • [26] Long-Distance Path Planning for Unmanned Surface Vehicles in Complex Marine Environment
    Shah, Brual C.
    Gupta, Satyandra K.
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2020, 45 (03) : 813 - 830
  • [27] Three-Dimensional Path Planning of UAVs in a Complex Dynamic Environment Based on Environment Exploration Twin Delayed Deep Deterministic Policy Gradient
    Zhang, Danyang
    Li, Xiongwei
    Ren, Guoquan
    Yao, Jiangyi
    Chen, Kaiyan
    Li, Xi
    SYMMETRY-BASEL, 2023, 15 (07):
  • [28] 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
  • [29] A Three-Dimensional Path Planning System for AUV Diving Process Considering Ocean Current and Energy Consumption
    Qu, Nanzhu
    Chen, Guanzhong
    Shen, Yue
    OCEANS 2021: SAN DIEGO - PORTO, 2021,
  • [30] An Information-Assisted Deep Reinforcement Learning Path Planning Scheme for Dynamic and Unknown Underwater Environment
    Xi, Meng
    Yang, Jiachen
    Wen, Jiabao
    Li, Zhengjian
    Lu, Wen
    Gao, Xinbo
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2025, 36 (01) : 842 - 853