Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (4): 1453-1466.doi: 10.13229/j.cnki.jdxbgxb.20230733

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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

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

CLC Number: 

  • TP391.9

Fig.1

Probability density map of search base points"

Fig.2

Contains a probability distribution plot"

Fig.3

Search and rescue area determination"

Fig.4

Search and rescue area considering deviation"

Fig.5

Square area contains the probability"

Fig.6

Search and rescue route planning"

Fig.7

Flow chart whale optimization algorithm"

Fig.8

Second-order Bézier curve optimization"

Fig.9

Wind field data information in the sea"

Fig.10

Temperature data information in the sea"

Fig.11

Algorithm runtime with different parameters"

Fig.12

Algorithm coverage with different parameters"

Fig.13

Simulation Results of route planning"

Table 1

Experimental result comparison under different algorithm parameters"

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

Fig.14

Objective functions of different schemes"

Table 2

Analysis of simulated data"

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

Table 3

Program decision analysis"

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

Fig.15

Track coverage under different methods"

Fig.16

Running time of different algorithms"

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