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Nautical access study based on real time bird’s eye view simulations

Mansuy M., Candries M., Eloot K.

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

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2021

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Vol. 15 No. 1

53--61

EN

Real time ship manoeuvring simulations are a valuable source of information in the detailed design phase of nautical studies. The feedback of pilots, which is not available for fast time simulations that are carried out by a computerized autopilot, is an important asset in the evaluation of the feasibility of ship manoeuvres. However, real time simulations are significantly more expensive in that realistic 3D visuals are needed so that the pilot can immerse himself in a sailing environment. Modelling and generating such 3D views is time consuming and requires expensive hardware and special skills. Real time simulations that offer only a 2D bird’s eye view for the execution of manoeuvres by pilots, can sometimes be used as a cheaper and faster alternative. This paper presents a case study that evaluates the nautical access to two harbours and discusses some of the advantages and disadvantages of a real time bird’s eye view setup.

2

Neuroevolutionary approach to COLREGs ship maneuvers

Łącki M.

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

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2019

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Vol. 13. no. 4

745--750

EN

The paper describes the usage of neuroevolutionary method in collision avoidance of two power-driven vessels approaching each other regarding COLREGs rules. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with artificial neural networks. The helmsman observes an environment by its input signals and according to assigned CORLEGs rule, he calculates the values of required parameters of maneuvers (propellers rpm and rudder deflection) in a collision avoidance situation. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task safely and efficiently. The main task of this project is to evolve a population of helmsmen which is able to effectively implement chosen rule: crossing or overtaking.

3

Indirect encoding in neuroevolutionary ship handling

Łącki M.

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

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2018

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Vol. 12, no. 1

71--76

EN

In this paper the author compares the efficiency of two encoding schemes for artificial intelligence methods used in the neuroevolutionary ship maneuvering system. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with an artificial neural network. The helmsman observes input signals derived form an enfironment and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of this project is to evolve a population of helmsmen with indirect encoding and compare results of simulation with direct encoding method.

4

Parameter identification of ship maneuvering models using recursive least square method based on support vector machines

Zhu M., Hahn A., Wen Y., Bolles A.

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

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2017

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Vol. 11, no. 1

23--29

EN

Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.