Intersection Crossing of Cooperative Multi-vessel Systems

被引:11
作者
Chen, Linying [1 ]
Negenborn, Rudy R. [1 ]
Hopman, Hans [1 ]
机构
[1] Delft Univ Technol, Dept Maritime & Transport Technol, Delft, Netherlands
关键词
Cooperative Multi-vessel System; Waterway Intersection Control; Vessel Train Formations; Distributed Model Predictive Control; MODEL-PREDICTIVE CONTROL;
D O I
10.1016/j.ifacol.2018.07.062
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A Cooperative Multi-Vessel System (CMVS) is a system consisting of multiple coordinated vessels. Vessels utilize Vessel-2-Vessel and Vessel-2-Infrastructure communication to making decisions with negotiating and\or collaborating with each other for a common goal. Due to the geographic limitations of banks and navigation rules and regulations, in straight waterways, the cooperation of vessels usually results in train-like formations. This behavior is similar to the highway platooning of vehicles. A particular challenge arises when such platoons have to cross waterway intersections. At the intersections, the vessel trains need to interact with others. However, research on the interaction between vehicle platoons is still lacking. This paper focuses on the cooperation of vessels at waterway intersections. We propose a framework for cooperative scheduling and control of CMVSs at intersections. The actions of the vessels are determined by solving two problems: Waterway Intersection Scheduling (WIS) and Vessel Train Formation (VTF). Firstly, the process of the vessels passing through an intersection is regarded as consumption of space and time. The WIS helps to find a conflict-free schedule for the vessels from different directions. By solving the WIS problem, each vessel's desired time of arrival can be determined. Then, the actions of vessels are determined using a distributed Model Predictive Control algorithm in the VTF problem. Agreement among the vessels is achieved via serial iterative negotiations. Simulation experiments are carried out to illustrate the effectiveness of the proposed framework. We compare the passing time of each vessel, and the total passing time in three scenarios: non-cooperative case, partially-cooperative case, and fully-cooperative case. With the proposed cooperative framework, vessels can have smoother trajectories. The total passing time and the passing time for each vessel also benefit from the cooperation. Besides, the proposed framework can be extended to the whole waterway network where other infrastructure (bridges and locks) exists. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:379 / 385
页数:7
相关论文
共 17 条
[1]   NMPC-based Trajectory Tracking and Collison Avoidance of Underactuated Vessels with Elliptical Ship Domain [J].
Abdelaal, Mohamed ;
Fraenzle, Martin ;
Hahn, Axel .
IFAC PAPERSONLINE, 2016, 49 (23) :22-27
[2]  
Chen L, 2018, TECHNICAL REPORT
[3]   Decentralized receding horizon control and coordination of autonomous vehicle formations [J].
Keviczky, Tamas ;
Borrelli, Francesco ;
Fregene, Kingsley ;
Godbole, Datta ;
Balas, Gary J. .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2008, 16 (01) :19-33
[4]   Mixed Integer Programming models for job shop scheduling: A computational analysis [J].
Ku, Wen-Yang ;
Beck, J. Christopher .
COMPUTERS & OPERATIONS RESEARCH, 2016, 73 :165-173
[5]  
Kuo T.C., 2006, J MAR SCI TECH-JAPAN, V14, P155
[6]   Person Re-Identification by Cross-View Multi-Level Dictionary Learning [J].
Li, Sheng ;
Shao, Ming ;
Fu, Yun .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (12) :2963-2977
[7]   Disturbance Compensating Model Predictive Control With Application to Ship Heading Control [J].
Li, Zhen ;
Sun, Jing .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (01) :257-265
[8]  
Linying Chen, 2016, Computational Logistics. 7th International Conference, ICCL 2016. Proceedings: LNCS 9855, P65, DOI 10.1007/978-3-319-44896-1_5
[9]   Multi-agent model predictive control for transportation networks: Serial versus parallel schemes [J].
Negenborn, R. R. ;
De Schutter, B. ;
Hellendoorn, J. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2008, 21 (03) :353-366
[10]   Distributed Model Predictive Control AN OVERVIEW AND ROADMAP OF FUTURE RESEARCH OPPORTUNITIES [J].
Negenborn, R. R. ;
Maestre, J. M. .
IEEE CONTROL SYSTEMS MAGAZINE, 2014, 34 (04) :87-97