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1

The Role of the European Maritime Simulator Network in Assessing Dynamic Sea Traffic Management Principles

Rizvanolli A., Burmeister H.C., John O.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2015

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Vol. 9, no. 4

559--564

EN

The MONALISA 2.0 (ML 2.0) project aims to define the Sea Traffic Management concept (STM), where information is shared amongst all stakeholders in the maritime transport chain, including nautical officers, ports, administrations, etc. Thus, a communication and information centered approach for data exchange by System Wide Information Management principles changing from surface-based- to voyage-based-operations has been proposed. Amongst others, testing and verifying the feasibility and benefits of STM and its solutions shall be done in the European Maritime Simulator Network (EMSN), a macro simulation environment for ship handling simulators. This is an open IEEE 1278 standard network protocol enabling interactive communication between distributed simulation environments. Based on an introduction into ML 2.0, the proposed STM concept is introduced, its expected impacts are listed and Key Performance Objectives are derived. The backgrounds on the EMSN are given and it is shown how it can assist in assessing the impact of STM’s Key Performance Indicators.

2

Simulation Augmented Manoeuvring Design and Monitoring – a New Method for Advanced Ship Handling

Benedict K., Kirchhoff M., Gluch M., Fischer S., Schaub M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2014

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Vol. 8, no. 1

131--141

EN

A fast time simulation tool box is under development to simulate the ships motion with complex dynamic models and to display the ships track immediately for the intended or actual rudder or engine manoeuvre. Based on this approach the innovative “Simulation Augmented Manoeuvring Design and Monitoring” - SAMMON tool box will allow for (a) a new type of design of a manoeuvring plan as enhancement exceeding the common pure way point planning (b) an unmatched monitoring of ship handling processes to follow the underlying manoeuvring plan. During the manoeuvring process the planned manoeuvres can be constantly displayed together with the actual ship motion and the predicted future track which is based on actual input data from the ship’s sensors and manoeuvring handle positions. This SAMMON tool box is intended be used on board of real ships but it is in parallel an effective tool for training in ship handling simulators: (a) in the briefing for preparing a manoeuvring plan for the whole exercise in some minutes, (b) during the exercise run to see the consequences of the use of manoeuvring equipment even before the ship has changed her motion and (c) in debriefing sessions to discuss potential alternatives of the students decisions by simulating fast variations of their choices during the exercises. Examples will be given for results from test trials on board and in the full mission ship handling simulator of the Maritime Simulation Centre Warnemuende.

3

Speciation of Population in Neuroevolutionary Ship Handling

Lacki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2010

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Vol. 4, no. 2

211--216

EN

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behavior in ship maneuvering. Simulated helmsman is treated as an individual in population, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current situation and choose one of the available actions. The individual improves his fitness function with reaching destination and decreases its value for hitting an obstacle. Neuroevolutionary approach is used to solve this task. Speciation of population is proposed as a method to secure innovative solutions.

4

On the Control of CPP Ships by Steering During In-Harbour Ship-Handling

Yabuki H., Yoshimura Y.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2010

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Vol. 4, no. 2

157--162

EN

This paper describes the results of experimental and simulation studies that aimed at developing effective control methods for single-CPP single-rudder ships during the coasting manoeuvre and the stopping manoeuvre. In order to improve the manoeuvrability of CPP ships under coasting, the authors performed full-scale experiments and confirmed that CPP ships under coasting using the Minimum Ahead Pitch (MHP) of CPP are controllable by steering. A simulation study was also conducted to evaluate the ship-handling method during the stopping manoeuvre that applies a turning moment to the ship by the maximum rudder angle steering prior to the reversing operation of the CPP and it is confirmed that CPP ships can be controlled sufficiently by the proposed method.

5

Reinforcement Learning in Ship Handling

Łącki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2008

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Vol. 2, no. 2

157--160

EN

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.

6

An Application of ANN to Automatic Ship Berthing Using Selective Controller

Im N., Seong-Jong Lee, Hyung-Do B.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2007

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Vol. 1, no. 1

101--105

EN

This paper deals with ANN(Artificial Neural Networks) and its application to automatic ship berthing. As ship motions are expressed by a multi-term non-linear model, it is very difficult to find optimal methods for automatic ship berthing. When a ship makes its berthing operation, the ship’s inertia and slow motion make the ship approach to final berthing point with pre-determined navigation pattern. If the ship is out of the pre-determined navigation pattern, the berthing usually end in failure. It has been known that the automatic control for ship’s berthing cannot cope with various berthing situations such as various port shape and approaching directions. For these reasons, the study on automatic berthing using ANN usually have been carried out based on one port shape and predetermined approaching direction. In this paper, new algorithm with ANN controller was suggested to cope with these problems. Under newly suggested algorithm, the controller can select different weight on the link of neural networks according to various situations, so the ship can maintain stable berthing operation even in different situations. Numerical simulations are carried out with this control system to find its improvement.