吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (4): 1453-1466.doi: 10.13229/j.cnki.jdxbgxb.20230733

• 通信与控制工程 • 上一篇    下一篇

海上被动无人搜救路线规划方法

张寒1(),黄炎焱1(),耿泽1,陈天德2   

  1. 1.南京理工大学 自动化学院,南京 210094
    2.中国电子科技集团第二十八研究所,南京 210007
  • 收稿日期:2023-07-13 出版日期:2025-04-01 发布日期:2025-06-19
  • 通讯作者: 黄炎焱 E-mail:zhanghan368@163.com;huangyy@njust.edu.cn
  • 作者简介:张寒(1994-),男,博士研究生.研究方向:控制与决策分析,优化与决策理论.E-mail: zhanghan368@163.com
  • 基金资助:
    信息系统需求重点实验室开放基金项目(LHZZ2021-M05);国家自然科学基金项目(61374186)

Passive unmanned maritime search and rescue routing method

Han ZHANG1(),Yan-yan HUANG1(),Ze GENG1,Tian-de CHEN2   

  1. 1.School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
    2.The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210007,China
  • Received:2023-07-13 Online:2025-04-01 Published:2025-06-19
  • Contact: Yan-yan HUANG E-mail:zhanghan368@163.com;huangyy@njust.edu.cn

摘要:

针对海上被动无人搜救任务中目标区域难以确定、搜索范围广泛、搜救区域航路规划速度慢等问题,提出了一种面向被动无人搜救任务的海上搜救区域规划流程及航路规划模型。根据海上搜救任务预案与航路规划需求,并结合海上搜救区域规划方法构建航路优选模型,利用搜救区域覆盖效率和搜救航路消耗成本等约束进行目标函数构建,采用鲸鱼优化算法求解,设计想定实验进行分析验证。研究表明:本文提出的海上被动无人搜救航路规划模型能够快速确定搜救范围,较好地搜索到代价更低的搜救路线。

关键词: 导航制导与控制, 海上搜救, 路线规划, 辅助决策

Abstract:

Addressing the challenges inherent in passive unmanned search and rescue missions at sea, including difficulties in target identification, broad search areas, and slow route planning, a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning, an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the whale optimization algorithm. The validity of the model is confirmed through designated scenario experiments, indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs.

Key words: navigation guidance and control, maritime search and rescue, route planning, auxiliary decision-making

中图分类号: 

  • TP391.9

图1

搜寻基点的初始位置概率密度分布图"

图2

包含概率分布图"

图3

基于基准点的搜救区域确定"

图4

考虑基准偏差的搜救区域设计"

图5

搜救正方形区域包含概率分布示意图"

图6

搜救航路规划示意图"

图7

鲸鱼优化算法流程"

图8

二阶贝塞尔曲线优化过程"

图9

海域内风场数据信息"

图10

海域内温度数据信息"

图11

不同参数下的鲸鱼算法运行时间"

图12

不同参数下的鲸鱼算法覆盖效率"

图13

航路规划仿真结果"

表1

不同算法参数下的实验结果对比分析"

序号种群规模迭代次数覆盖效率/%算法时间/ms
1105082.10781.08
21010085.961 411.06
31015086.612 017.34
4255096.30759.24
52510096.251 419.60

6

7

8

9

25

40

40

40

150

50

100

150

96.28

98.96

99.14

99.53

2 279.33

775.24

1 444.98

2 110.78

图14

不同方案下的目标函数结果"

表2

仿真结果统计"

方案

包含

概率/%

目标

函数值

算法

时间/ms

核心区域航路节点
方案A96.890.820 96 23720
方案B95.770.832 86 16421
方案C95.310.819 26 58228
方案D98.050.823 36 89425
方案E96.920.815 66 09124
方案F97.000.818 06 45724

表3

方案决策分析"

方案W+距离W-距离综合得分方案排名
方案A0.643 80.477 00.125 65
方案B0.510 80.544 90.152 44
方案C0.521 90.340 30.116 56
方案D0.582 90.635 10.153 93
方案E0.127 10.773 20.253 61
方案F0.294 50.599 00.197 92

图15

不同方法下的航迹覆盖情况"

图16

不同方法下的算法运行时间"

[1] 孙万, 柳堤, 李政. 我国海上人命救助现状、问题和对策初探[J]. 今日科苑, 2021(7): 26-32.
Sun Wan, Liu Di, Li Zheng. Preliminary study on current status, problems, and potential innovations of maritime life salvage in China[J]. Modern Science, 2021(7): 26-32.
[2] 朱岿, 牟林, 王道胜, 等. 海上搜救辅助决策技术研究进展[J]. 应用海洋学学报, 2019, 38(3): 440-449.
Zhu Kui, Mou Lin, Wang Dao-sheng, et al. Advance in maritime search and rescue decision support techniques[J]. Journal of Applied Oceanography, 2019, 38(3): 440-449.
[3] 王秀玲, 尹勇, 赵延杰, 等. 无人艇海上搜救路径规划技术综述[J]. 船舶工程, 2023, 45(4): 50-57.
Wang Xiu-ling, Yin Yong, Zhao Yan-jie, et al. Overview of USV maritime search and rescue path planning technology[J]. Ship Engineering, 2023, 45(4): 50-57.
[4] 李宁, 张强. 基于北斗系统的海上应急救援系统的研究[J]. 中国海事, 2019(7): 49-51.
Li Ning, Zhang Qiang. On the emergent maritime search and rescue system based on the Beidou System [J]. China Maritime Safety, 2019(7): 49-51.
[5] 陈天德, 黄炎焱, 沈炜. 基于虚拟障碍物法的无震荡航路规划[J]. 兵工学报, 2019, 40(3): 651-658.
Chen Tian-de, Huang Yan-yan, Shen Wei. Non-oscillation path planning based on virtual obstacle method[J]. Acta Armamentarii, 2019, 40(3):651-658.
[6] Jiang T, Lin D, Song T. Finite-time control for small-scale unmanned helicopter with disturbances[J]. Nonlinear Dynamics, 2019, 96: 1747-1763.
[7] Melsom A, Counillon F, La Casce J H, et al. Forecasting search areas using ensemble ocean circulation modeling[J]. Ocean Dynamics, 2012, 62: 1245-1257.
[8] Cho S W, Park H J, Lee H, et al. Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations[J]. Computers & Industrial Engineering, 2021, 161: No.107612.
[9] Ai B, Jia M, Xu H, et al. Coverage path planning for maritime search and rescue using reinforcement learning[J]. Ocean Engineering, 2021, 241: No. 110098.
[10] Larson J, Bruch M, Ebken J. Autonomous navigation and obstacle avoidance for unmanned surface vehicles[C]∥Unmanned Systems Technology VIII,Orlando,USA, 2006, 6230: 53-64.
[11] Campbell S, Naeem W, Irwin G W. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres[J]. Annual Reviews in Control, 2012, 36(2): 267-283.
[12] 熊勇, 余嘉俊, 张加, 等. 无人艇研究进展及发展方向[J]. 船舶工程, 2020, 42(2): 12-19.
Xiong Yong, Yu Jia-jun, Zhang Jia, et al. Research progress and development direction of unmanned boat[J]. Ship Engineering, 2020, 42(2): 12-19.
[13] 王浩亮, 尹晨阳, 卢丽宇, 等. 面向海上搜救的UAV与USV集群协同路径跟踪控制[J]. 中国舰船研究, 2022, 17(5): 157-165.
Wang Hao-liang, Yin Chen-yang, Lu Li-yu, et al. Cooperative path following control of UAV and USV cluster for maritime search and rescue[J]. Chinese Journal of Ship Research, 2022, 17(5): 157-165.
[14] Nash L, Hover G L, Burns R E. Additional analysis of probability of detection(POD) in search and rescue (SAR) project data[R].New London: Analysis and Technology, 1982.
[15] 李家林, 张建强, 李春来. 基于优化人工势场法的无人艇局部路径规划[J]. 舰船科学技术, 2022, 44(16): 69-73.
Li Jia-lin, Zhang Jian-Qiang, Li Chun-lai. Local path planning of unmanned boat based on optimized artificial potential field method[J]. Ship Science and Technology, 2022, 44(16): 69-73.
[16] Breivik Ø, Allen A A, Maisondieu C, et al. Wind-induced drift of objects at sea: the leeway field method[J]. Applied Ocean Research, 2011, 33(2): 100-109.
[17] 王军, 于安民, 杨春林. 海上搜寻船舶选择问题研究[J]. 重庆交通大学学报:自然科学版, 2021, 40(3):7-15.
Wang Jun, Yu An-min, Yang Chun-lin. Selection problem of search ship at sea[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(3): 7-14.
[18] 李鸿一, 陈锦涛, 任鸿儒, 等. 基于随机采样的高层消防无人机协同搜索规划[J]. 中国科学: 信息科学,2022, 52(9): 1610-1626.
Li Hong-yi, Chen Jin-tao, Ren Hong-ru, et al. Random-sampling-based multi-UAV cooperative search planning for high-rise firefighting[J]. Science in China(Information Sciences), 2022, 52(9): 1610-1626.
[19] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[20] 周寅飞, 张立华, 贾帅东, 等. 最大可航窗口序列约束贝塞尔曲线的无人船自主航行航线规划方法[J].武汉大学学报: 信息科学版, 2024, 49(7): 1224-1236.
Zhou Yin-fei, Zhang Li-hua, Jia Shuai-dong, et al. Autonomous navigation route planning method of unmanned ship based on bessel curves constrained by maximum navigable window sequence[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1224-1236.
[21] 王晓光, 何晓夫, 武学祺. 基于多属性折中决策的空面制导弹药配置方案评价与优选[J]. 弹箭与制导学报, 2019, 39(1): 139-142.
Wang Xiao-guang, He Xiao-fu, Wu Xue-qi. Function evaluation and optimal selection of air-surface guided ammunition configuration based on VIKOR [J]. Journal of Projectiles,Rockets,Missiles and Guidance, 2019, 39(1): 139-142.
[22] 袁书泽. 海难人命救助的有效时限初步探讨[J]. 中国医药科学, 2011, 11(1): 146,149.
Yuan Shu-ze. Preliminary discussion on the effective time limit of life-saving disaster relief[J]. China Medicine and Pharmacy, 2011, 11(1): 146,149.
[23] 任东彦. 海上搜救力量配置问题研究[J]. 舰船电子工程, 2022, 42(1): 19-22.
Ren Dong-yan. Research on configuration of search and rescue forces at sea[J]. Ship Electronic Engineering, 2022, 42(1): 19-22.
[24] 罗志远, 丰硕, 刘小峰, 等. 一种基于分步遗传算法的多无人清洁车区域覆盖路径规划方法[J]. 电子测量与仪器学报, 2020, 34(8): 43-50.
Luo Zhi-yuan, Feng Shuo, Liu Xiao-feng, et al. Method of area coverage path planning of multi-unmanned cleaning vehicles based on step by step genetic algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(8): 43-50.
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