河口深槽可航宽度变化水域航行决策方法

doi:10.16183/j.cnki.jsjtu.2023.356

摘要/Abstract

摘要：

为解决河口深槽可航宽度变化水域船舶航行决策挑战,以长江口北槽深水航道为例,围绕环境数字孪生、航行规则融入、船舶操纵性限制和特殊水域下避碰机理等关键科学问题开展研究.将环境构成要素分类、建模并实现环境数字孪生.归纳、量化解析航行规则要求并融入决策过程, 提出本船非线性操纵特性下的控制和过程预测方法.探究基于研究水域船舶行为特征的避碰机理,建立可行航向航速范围求取方法,提出多因素约束下能动态自适应系统剩余误差和目标船随机运动的航行决策方法.在预设场景下,本船在241、1 484、4 119 s 时,分别右转7°、右转2° 且将车令降到前进1、右转5° 且将车令降到前进1可安全通过.结果表明,提出的方法能在研究场景中精确进行航行决策,安全避让并及时跟踪航线.

关键词: 航行决策, 河口深槽水域, 避碰机理, 航向航速控制系统, 数字孪生环境

Abstract:

To address the challenges of ship navigation decision-making in the water area where the navigable width of the estuary deep channel changes, taking the north channel deep waterway of the Yangtze River estuary as an example, research is conducted on the key scientific issues such as environmental digital twin, navigation rule integration, ship maneuverability limitation, and collision avoidance mechanism in special water area. First, the environmental components are classified, modeled, and implented into an environmental digital twin. Navigation requirements are summarized, quantitatively analyzed, and integrated into the decision-making process. A control and process prediction method for the nonlinear maneuvering characteristics of the own ship is proposed. Then, collision avoidance mechanism specific to the ship behavior characteristics of the research water area is explored. A method for obtaining the feasible heading and speed range is established, and a dynamic navigation decision-making method is proposed, which is capable of adapting to the system residual error and the random motion of the target ship under multiple constraints. In preset scenarios, the method proposed ensures all safe passage targets with adjustments of course 7° to starboard, course 2° to starboard with a telegraph order reduction to forward 1, course 5° to starboard with a telegraph order reduction to forward 1 at 241 s, 1 484 s, and 4 119 s respectively. The results show that the proposed method can accurately make navigation decisions, ensure collision avoidance, and perform route tracking in a timely manner.

Key words: navigation decision, deep trough waters of estuary, collision avoidance mechanism, course speed control system, digital twin environment

中图分类号:

U675.96

贺益雄, 代永刚, 赵兴亚, 于德清, 黄立文. 河口深槽可航宽度变化水域航行决策方法[J]. 上海交通大学学报, 2025, 59(4): 489-502.

HE Yixiong, DAI Yonggang, ZHAO Xingya, YU Deqing, HUANG Liwen. Navigation Decision-Making Method in Estuary Deep Trough with Varying Width of Navigable Waters[J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 489-502.

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参考文献 25

[1]	张英俊, 翟鹏宇. 海运船舶自主避碰技术研究进展与趋势[J]. 大连海事大学学报, 2022, 48(3): 1-11.
	ZHANG Yingjun, ZHAI Pengyu. Research progress and trend of autonomous collision avoidance technology for maritime ships[J]. Journal of Dalian Maritime University, 2022, 48(3): 1-11.
[2]	CHEN P, HUANG Y, MOU J, et al. Ship collision candidate detection method: A velocity obstacle approach[J]. Ocean Engineering, 2018, 170: 186-198.
[3]	张笛, 赵银祥, 崔一帆, 等. 智能船舶的研究现状可视化分析与发展趋势[J]. 交通信息与安全, 2021, 39(1): 7-16.
	ZHANG Di, ZHAO Yinxiang, CUI Yifan, et al. Visualization analysis and development trend of intelligent ship research status[J]. Traffic Information and Safety, 2021, 39(1): 7-16.
[4]	HE Y, LIU X, ZHANG K, et al. Dynamic adaptive intelligent navigation decision making method for multi-object situation in open water[J]. Ocean Engineering, 2022, 253: 111238.
[5]	马小轩, 吴韵哲, 吴浩峻, 等. 基于改进人工势场法的水下自主航行器路径规划[J]. 船舶工程, 2021, 43(9): 89-93.
	MA Xiaoxuan, WU Yunzhe, WU Haojun, et al. Path planning of underwater autonomous vehicle based on improved artificial potential field method[J]. Ship Engineering, 2021, 43(9): 89-93.
[6]	丁志国, 张新宇, 王程博, 等. 基于驾驶实践的无人船智能避碰决策方法[J]. 中国舰船研究, 2021, 16(1): 96-104.
	DING Zhiguo, ZHANG Xinyu, WANG Chengbo, et al. Intelligent collision avoidance decision method for unmanned ships based on driving practice[J]. China Ship Research, 2021, 16(1): 96-104.
[7]	刘钊, 周壮壮, 张明阳, 等. 基于双延迟深度确定性策略梯度的船舶自主避碰方法[J]. 交通信息与安全, 2022, 40(3): 60-74.
	LIU Zhao, ZHOU Zhuangzhuang, ZHANG Ming-yang, et al. Ship autonomous collision avoidance method based on double delay depth deterministic strategy gradient[J]. Traffic Information and Safety, 2022, 40(3): 60-74.
[8]	李丽娜, 陈国权, 杨凌波, 等. 船舶拟人智能避碰决策算法测试及应用[J]. 中国航海, 2022, 45(1): 1-7.
	LI Lina, CHEN Guoquan, YANG Lingbo, et al. Testing and application of intelligent collision avoidance algorithm for ship personification[J]. Navigation of China, 2022, 45(1): 1-7.
[9]	ZHANG K, HUANG L, LIU X, et al. A novel decision support methodology for autonomous collision avoidance based on deduction of manoeuvring process[J]. Journal of Marine Science and Engineering, 2022, 10(6): 765-789.
[10]	LI M, MOU J, CHEN L, et al. A rule-aware time-varying conflict risk measure for MASS considering maritime practice[J]. Reliability Engineering and System Safety, 2021, 215: 107816-107836.
[11]	谢鸿伟, 张英俊, 邢胜伟, 等. 基于模型预测控制的船舶自主避碰方法[J]. 船舶工程, 2021, 43(8): 23-28.
	XIE Hongwei, ZHANG Yingjun, XING Shengwei, et al. Ship autonomous collision avoidance method based on model predictive control[J]. Ship Engineering, 2021, 43(8): 23-28.
[12]	朱凯歌, 史国友, 汪琪, 等. 基于船舶领域的碰撞危险度评估模型[J]. 上海海事大学学报, 2020, 41(2): 1-5.
	ZHU Kaige, SHI Guoyou, WANG Qi, et al. Collision risk assessment model based on ship field[J]. Journal of Shanghai Maritime University, 2020, 41(2): 1-5.
[13]	中华人民共和国港务监督局. 1972年国际海上避碰规则[M]. 北京: 人民交通出版社, 1998.
	Port Supervision Authority of the People’s Republic of China. International regulations for preventing collisions at sea, 1972[M]. Beijing: People’s Communications Press, 1998.
[14]	黄立文, 李浩宇, 梁宇, 等. 基于操纵过程推演的船舶可变速自动避碰决策方法[J]. 交通信息与安全, 2021, 39(6): 1-10.
	HUANG Liwen, LI Haoyu, LIANG Yu, et al. Decision-making method of automatic collision avoidance for ships with variable speed based on maneuvering process inference[J]. Traffic Information and Safety, 2021, 39(6): 1-10.
[15]	周田瑞, 邵哲平, 潘家财, 等. 基于AIS数据挖掘的受限水域船舶动态领域研究[J]. 集美大学学报(自然科学版), 2018, 23(1): 33-38.
	ZHOU Tianrui, SHAO Zheping, PAN Jiacai, et al. Research on ship dynamics in restricted waters based on AIS data mining[J]. Journal of Jimei University (Natural Science Edition), 2018, 23(1): 33-38.
[16]	XIA Y, ZHENG S, YANG Y, et al. Ship maneuvering performance prediction based on MMG model[J]. IOP Conference Series: Materials Science and Engineering, 2018, 452(4): 042046.
[17]	卢贤续. 风浪影响下大型邮轮电力推进系统操纵特性仿真[D]. 哈尔滨: 哈尔滨工程大学, 2021.
	LU Xianxu. Simulation of control characteristics of electric propulsion system of large cruise ship under the influence of wind and waves[D]. Harbin: Harbin Engineering University, 2021.
[18]	鞠世琼. 船舶航迹舵控制技术研究与设计[D]. 哈尔滨: 哈尔滨工程大学, 2007.
	JU Shiqiong. Research and design of ship track rudder control technology[D]. Harbin: Harbin Engineering University, 2007.
[19]	DYDA A A, CHINCHUKOVA E P, OSKIN D A, et al. An effective and simple adaptive algorithm for ship course control systems[C]// International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). Vladivostok,Russia: IEEE, 2019: 1-4.
[20]	NEKOO R S. Nonlinear closed loop optimal control: A modified state-dependent Riccati equation[J]. ISA Transactions, 2013, 52(2): 285-290. doi: 10.1016/j.isatra.2012.10.005 pmid: 23158930
[21]	张显库, 任俊生, 张秀凤. 船舶建模与控制[M]. 大连: 大连海事大学出版社, 2014.
	ZHANG Xianku, REN Junsheng, ZHANG Xiufeng. Ship modeling and control[M]. Dalian: Dalian Maritime University Press, 2014.
[22]	贺益雄, 张晓寒, 胡惟璇, 等. 基于航向控制系统的船舶动态避碰机理[J]. 西南交通大学学报, 2020, 55(5): 988-993.
	HE Yixiong, ZHANG Xiaohan, HU Weixuan, et al. Dynamic collision avoidance mechanism of ship based on course control system[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 988-993.
[23]	贺益雄, 于德清, 刘霄, 等. 成山角分道通航制水域航行风险辨识与操纵决策方法[J]. 交通信息与安全, 2022, 40(5): 34-43.
	HE Yixiong, YU Deqing, LIU Xiao, et al. A method of risk identification and decision-making support for ship maneuvers at Chengshanjiao waters under traffic separation scheme[J]. Traffic Information and Safety, 2022, 40(5): 34-43.
[24]	宁君, 李学健, 李伟, 等. 基于改进人工势场法的船舶自动避碰研究[J]. 舰船科学技术, 2021, 43(23): 59-64.
	NING Jun, LI Xuejian, LI Wei, et al. Research on ship automatic collision avoidance based on improved artificial potential field method[J]. Ship Science and Technology, 2021, 43(23): 59-64.
[25]	贺益雄, 于德清, 刘霄, 等. 成山角水域动态自适应自主航行决策方法[J]. 哈尔滨工程大学学报, 2023, 44(10): 1680-1688.
	HE Yixiong, YU Deqing, LIU Xiao, et al. A dynamic adaptive autonomous navigation decision method for Chengshanjiao waters[J]. Journal of Harbin Engineering University, 2023, 44(10): 1680-1688.

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环境要素类型	要素模型	要素状态
钻井平台、岛屿	圆形	静态
浮标、明(暗)礁	点状	静态
浅滩、岸线	多边形	静态
常规机动船	普通机动船	动态
拖带船、拖网渔船	条形	动态
操限、失控船	圆形	动态
非机动船	点状	动态

船舶类别	位置坐标/(°)	航向/(°)	航速/kn	船长/m
本船	(122.495, 31.104)	270	12	225
目标船1	(122.470, 31.104)	270	6	80
目标船2	(122.369, 31.118)	140	10	80
目标船3	(122.295, 31.110)	113	5	80
目标船4	(122.330, 31.094)	90	9	85
目标船5	(122.381, 31.101)	90	10	85

船舶类别	位置坐标/(°)	航向/(°)	航速/kn	船长/m
本船	(122.122, 31.215)	300	12	225
目标船1	(122.110, 31.222)	305	6	80
目标船2	(122.044, 31.250)	205	5.4	85
目标船3	(121.018, 31.256)	245	2	50
目标船4	(121.978, 31.249)	102	9	85
目标船5	(122.053, 31.227)	108	8	85