基于泊位坐标的无人船自主靠泊仿真研究

doi:10.16182/j.issn1004731x.joss.20-FZ0303

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (10): 1910-1917.doi: 10.16182/j.issn1004731x.joss.20-FZ0303

基于泊位坐标的无人船自主靠泊仿真研究

贾玉鹏¹, 尹勇¹, 朱忠显²

1.大连海事大学航海动态仿真和控制交通部重点实验室,辽宁大连 116026;
2.大连海事大学轮机工程学院,辽宁大连 116026

收稿日期:2020-02-22 修回日期:2020-06-04 出版日期:2020-10-18 发布日期:2020-10-14
作者简介:贾玉鹏(1996-),男,山西临汾,硕士生,研究方向为海上智能交通系统;尹勇(1969-),男,湖北郧县,博士,教授,博导,研究方向为航海仿真、航海智能。
基金资助:
高技术船舶科研项目(2018-473),中央高校基本科研业务费(3132019312)

Research on Autonomous Berthing for Unmanned Ship Based on Berth Coordinates

Jia Yupeng¹, Yin Yong¹, Zhu Zhongxian²

1. Key Laboratory of Marine Simulation &Control for Ministry of Transportation, Dalian 116026, China;
2. Marine Engineering College, Dalian Maritime University, Dalian 116026, China

Received:2020-02-22 Revised:2020-06-04 Online:2020-10-18 Published:2020-10-14

摘要/Abstract

摘要： 针对神经网络自主靠泊控制器只能完成特定港口的靠泊,而无法拓展到其他没有训练数据的泊位这一问题,提出一种以泊位顶点为原点,岸线为y轴的坐标系(泊位坐标系)。采用泊位坐标系中相对位置来训练神经网络控制器,在V.Dragon-5000航海模拟器中选取集装箱船“银河号”,在大连港进行靠泊训练后,成功将靠泊控制器拓展到没有训练数据的深圳蛇口港与新加波樟宜港。仿真验证了采用新建立的坐标系可以拓展神经网络自主靠泊控制器适用范围,减小训练数据所需的成本。

关键词: 无人船, 自主靠泊, 泊位坐标, 神经网络控制器, 航海模拟器

Abstract: Aiming at the problem that the neural network autonomous docking controllers can only complete the docking of a specific ports, but cannot extend to other ports without training data, a coordinate system (berth coordinates with the berth vertex as theorigin and the shoreline as the Y Axis) is proposed. The relative position is used to train the controller. In V.Dragon-5000 navigation simulator, the container ship “Yinhe” is selected. After docking training at Dalian port, the docking controller is successfully extended to Shenzhen Shekou port and Singapore Changi port without training data. The simulation verifies that the application of the newly established coordinate system can greatly expand the application range of the neural network autonomous docking controller and reduce the cost required for training data.

Key words: unmanned ship, automatic ship berthing, berth coordinates, ANN controller, navigation simulation

中图分类号:

U675.79

贾玉鹏, 尹勇, 朱忠显. 基于泊位坐标的无人船自主靠泊仿真研究[J]. 系统仿真学报, 2020, 32(10): 1910-1917.

Jia Yupeng, Yin Yong, Zhu Zhongxian. Research on Autonomous Berthing for Unmanned Ship Based on Berth Coordinates[J]. Journal of System Simulation, 2020, 32(10): 1910-1917.

参考文献 16

[1]	李晔, 贾知浩, 张伟斌, 等. 面向无人艇自主靠泊的分层轨迹规划与试验[J]. 哈尔滨工程大学报, 2019, 40(6): 1043-1050.Li Ye, Jia Zhihao, Zhang Weibin, et al.Layered trajectory planning and experiment for auto-berthing of unmanned surface vehicle[J]. Journal of Harbin Engineering University, 2019, 40(6): 1043-1050.
[2]	卜仁祥, 刘正江, 胡江强. 欠驱动船舶非线性滑模靠泊控制器[J]. 交通运输工程学报, 2007, 7(4): 24-29.Bu Renxiang, Liu Zhengjiang, Hu Jiangqiang.Berthing controller of under actuated ship with nonlinear sliding mode[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 24-29.
[3]	Park J Y, Kim N.Design of an adaptive backstepping controller for auto-berthing a cruise ship under wind loads[J]. International Journal of Naval Architecture and Ocean Engineering (S2092-6782), 2014, 6(2): 347-360.
[4]	Ahmed Y A, Hasegawa K.Automatic ship berthing using artificial neural network based on virtual window concept in wind condition[C]// 13th IFAC Symposium on Control in Transportation Systems. Oxford, UK: IF-AC-Elsevier Ltd, 2012: 12-14.
[5]	Im N, Hasegawa K.A Study on Automatic Ship Berthing Using Parallel Neural Controller (2nd Report): Motion Identification Considering Lateral Speed and Angular Velocity to cope with Disturbances[J]. Journal of the Kansai Society of Naval Architects Japan (S1880-3717), 2002, 2002(237): 127-132.
[6]	Ahmed Y A, Hasegawa K.Implementation of automatic ship berthing using artificial neural network for free running experiment[C]// The 9th International Federation of Automatic Control (IFAC) Conference on Control Applications in Marine Systems (CAMS). Oxford, UK: IFAC-Elsevier Ltd, 2013: 25-30.
[7]	Ahmed Y A, Hasegawa K.Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method[J]. Engineering Applications of Artificial Intelligence (S0572-1976), 2013, 26(10): 2287-2304.
[8]	Ahmed Y A, Hasegawa K.Artificial neural network based automatic ship berthing combining PD controlled side thrusters—A combined controller for final approaching to berth[C]// 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). Singapore: IEEE, 2014: 1304-1309.
[9]	Mizuno N.Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator[J]. Control Engineering Practice (S0967-0661), 2007, 15(6): 757-765.
[10]	Mizuno N, Uchida Y, Okazaki T.Quasi real-time optimal control scheme for automatic berthing[J]. IFAC PapersOnLine (S2405-8963), 2015, 48(16): 305-312.
[11]	Mizuno N, Kakami H, Okazaki T.Parallel simulation based predictive control scheme with application to approaching control for automatic berthing[J]. IFAC Proceedings Volumes (S1474-6670), 2012, 45(27): 19-24.
[12]	Mizuno N, Saka N, Katayama T.A ship's automatic maneuvering system using optimal preview sliding mode controller with adaptation mechanism[J]. IFAC PapersOnLine (S2405-8963), 2016, 49(23): 576-581.
[13]	Van-Suong Nguyen, Van-Cuong Do, Nam-Kyun Im.Development of automatic ship berthing system using artificial neural network and distance measurement system[J]. International Journal of Fuzzy Logic and Intelligent Systems (S2093-744X), 2018, 18(1): 41-49.
[14]	Im N K, Nguyen V S.Artificial neural network controller for automatic ship berthing using head-up coordinate system[J]. International Journal of Naval Architecture and Ocean Engineering (S2092-6782), 2018, 10(3): 235-249.
[15]	Qiang Z, Guibing Z, Xin H, et al.Adaptive neural network auto-berthing control of marine ships[J]. Ocean Engineering (S0029-8018), 2019, 177(ARP.1): 40-48.
[16]	张秀凤, 尹勇, 金一丞. 规则波中船舶运动六自由度数学模型[J]. 交通运输工程学报, 2007, 7(3): 40-43.Zhang Xiufeng, Yin Yong, Jin Yicheng.Ship motion mathematical model with six degrees of freedom in regular wave[J]. Journal of Traffic and Transportation Engineering, 2007, 7(3): 40-43.

基于泊位坐标的无人船自主靠泊仿真研究

Research on Autonomous Berthing for Unmanned Ship Based on Berth Coordinates

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