Zeszyty Naukowe 20(92) 71
Scientific Journals
Zeszyty Naukowe
Maritime University of Szczecin
Akademia Morska w Szczecinie
2010, 20(92) pp. 71–74 2010, 20(92) s. 71–74
Model of sea going ferries moorings manoeuvres decisions
Model decyzji manewrowych cumowania promów morskich
Adam Kowalski
Unity Line Ltd., 70-419 Szczecin, pl. Rodła 8 e-mail: kowalski@interia.eu
Key words: manoeuvres decisions, manoeuvring model, process of the model building Abstract
The paper presents the researches of manoeuvring model of the ferry m/f “Polonia” at the Ystad harbour. Manoeuvring decisions recognition system based on probabilistic model by using the Bayesian belief networks has been proposed. Suggested variables necessary for defined nodes of network, methods of converting into discrete variables have also been presented. Finally there are some conclusions and suggestions concerning constructed model.
Słowa kluczowe: decyzje manewrowe, model manewrowania, proces budowy modelu Abstrakt
W artykule przedstawiono model manewrowania promu m/f „Polonia” w porcie Ystad. Omówiono koncepcję budowy systemu rozpoznawania decyzji manewrowych, bazującego na wykorzystaniu sieci bayerowskich. Zaprezentowano zmienne konieczne do zdefiniowania węzłów sieci wraz z możliwościami ich dyskretyzacji. W zakończeniu artykułu zamieszczono spostrzeżenia i wnioski dotyczące procesu budowy modelu.
Introduction
Models of ferry moorings manoeuvres decision are based on assumption that humans are reason-able and capreason-able of solving a problem. In case of many possible decision options and preferences there is relatively high probability of making rea-sonably and effectively the same decision by most of people. This assumption can be used to build recognizing system based on experts’ knowledge and experience. Expert’s experience is very diffi-cult to transfer into formal mathematical models. It is reasonable to solve this problem by using other model for instance probabilistic model.
In the following research experts’ knowledge and experience have been used to construct a pro-babilistic model of the decision recognition system of the manoeuvring ferry at the port. The aim of the suggested research is to find the combination of settings of ferry propulsion and steering equipment at every moment of manoeuvres in the port for already defined combination of external conditions.
Vertical and horizontal restrictions of the navi-gating area have influence on captain’s manoeuvr-ing decision [1]. Many of these decisions may be presented in the form of mathematical models. Other factors, difficult to express by formal know-ledge, also have a significant influence on decisions such as for example the influence of complicated hydrodynamics in a restricted area. It is necessary to take into consideration the interaction between propulsions of the ferry – combination settings of bow thrusters, stern thruster, propellers and rudders. Not to mention the equipment and standard of tech-nical maintenance, crew experience and qualifica-tions or condition of navigational aids of the area, which should also be considered.
The above described probabilistic model should include the influence of all factors inclusive others definable only in thinking process of the manoeuvring captain. It has been proposed to use for discovering knowledge from ferry manoeuvring data by using Bayesian belief networks. Bayesian methods will convert experts’ experience into very
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72 Scientific Journals 20(92)
precise probabilistic model. The Bayesian belief networks are often used in reasoning systems [2]. They have proven themselves in a number of applications.
Sketch of the ferries manoeuvring decision recognition system
Manoeuvring decision recognition system should include many modules. The most important are the following:
– input module, – decision recognizer, – knowledge base, – output module.
The input module will transfer all necessary na-vigational signals that represent data into decision recognizer intelligible standard. Knowledge base will collect data from real time manoeuvres. Output module will show suggestion concerning settings of steering and propellers to manoeuvring Captain. Figure 1 shows an overview of the suggested archi-tecture of the system.
Continuous lines connecting modules describe static version of the system. Additional dashed line presents inserted correction feedback into the system.
Fig. 1. Manoeuvring decision recognition system Rys. 1. System rozpoznawania decyzji manewrowych
In this situation the system will receive self correcting and learning possibility.
The main part of the system is Decision Recog-nizer. As suggested at the beginning of the chapter, this module will be constructed by using Bayesian belief networks.
Object and place of research
M/f “Polonia” (Fig. 2) is using one of two unique ferry railway stands. The first stand is located in Świnoujście, the other in Ystad. In this situation manoeuvring area is restricted to these two ports. Successful research needs recurrent methods for solving this same manoeuvring problem. This
situation occurs when m/f “Polonia” is manoeuvr-ing more than 10 years in the port of Ystad. Both give chance to clearly define the Bayesian belief network probabilities. In other words one combi-nation of navigational external condition should entail high probability of only one combination of propulsion and steering settings.
Fig. 2. View of m/f “Polonia” Rys. 2. Widok na m/f „Polonia”
Successful construction of research concerning the ships manoeuvres needs objects able to carry out safely operation in all reasonable hydro meteorological condition. According to former research m/f “Polonia” is ready to undertake acceptable level of risk manoeuvres with force of wind less than 27 m/s [3]. This force of wind 10 degrees in Beaufort scale, appears very seldom but in this meteorological condition, restriction of ferry movement is derived from other things for example port regulations.
Other important factor of success of research is high quality of equipment and technical mainte-nance standard. All above mentioned necessary conditions of successful research are fulfilled for m/f “Polonia” manoeuvres at the port of Ystad.
Some technical and manoeuvring data are shown below:
– overall length 169.90 m; – breadth 28 m;
– GRT 29875, 4 main engines each 3960 kW, 2 controllable pitch propellers;
– 1 stern and 3 bow thrusters each of them 1600 kW;
– summer draft 6.20 m;
– crosswind pressure area 3250 m2.
BN should be derived from large quantity and quality of data. M/f “Polonia” is equipped with Ferry Navigational Anti-Collision System (FNAS) prepared by research team of the Maritime Traffic Engineering Institute of Maritime University in Szczecin. FNAS is a kind of Electronic Navigatio-nal Chart (ENC) with some function specially
Decision recognizer Output module Input module Knowledge base Settings of steerings and propellers
Na vig ati on al in fo rm ati on si gn als
Model of sea going ferries moorings manoeuvres decisions
Zeszyty Naukowe 20(92) 73
designed for pilot’s passages and manoeuvres. This system is connected, among other manoeuvring controls, to NMEA signal distributor. They all will supply full information about external conditions and manoeuvring settings. FNAS can collect data proposed for research in satisfying quality and quantity based on real time manoeuvring in the port. It seems also possible to insert manoeuvring decision recognition system into FNAS equipment. Concisely the most important propositions of the paper as well as the author’s views of the practical implications of the results.
Variables of the model
Identification of all the factors that have influence on captain’s manoeuvring decisions play important role in the research. These factors will be defined as variables of the manoeuvres decision recognition model. Four groups of variables have been suggested:
– external conditions, – parameters of the motion, – ferry position,
– settings of steering equipment and propellers. Most algorithms for Bayesian networks are designed for discrete variables. To take advantage of these algorithms, Bayesian network models in-clude discrete variables or conceptually continuous variables that have been discretized. Quantization step will be suggested for every variable according to practical influence on ferry motion and real manoeuvring decisions.
The set of variables that it is necessary to take into consideration in the research are given below.
The following variables describe external condi-tions:
– direction of the wind with 16 values N, NNE, NEWNW, W, etc.;
– force of wind from 0 m/s up to 25 m/s, with 5 m/s step;
– quantity of bow thrusters being used during manoeuvres: 1, 2 or 3;
– ferry amidships draft in meters with 0.10 m step, range from 5.70 m to 6.20 m;
– water level with 0.10 m step, mean water level 0. Last two variables can be in simplest model reduced to under keel clearance with step 0.10 m
The following variables describe parameters of motion:
– true course in degrees with 1 degree step; – change of true course – rate of turn with
5 degrees/minute step;
– course over the ground with 1 degree step; – change of the course over the ground with
5 degrees/minute step;
– speed over the ground in kts with 0.5 kts step; – change of the speed over the ground –
acceleration with 0.01 kts/s step.
The following variables describe ferry position:
– latitude with 0.001 of arc minute discretization step;
– longitude with 0.001 of arc minute discretization step.
The following variables describe settings of pro-pellers and steering:
– setting of bow thrusters in percent with 10% step; range from –100 (all to port) to +100 (all to starboard);
– setting of stern thruster in percent with 10% step; range from –100 (all to port) to +100 (all to starboard);
– setting of starboard propeller in percent of full setting with 5% step, range from 0 to 100; – ahead/astern of starboard engines with 2 values:
ahead and eastern;
– setting of main starboard helm in degrees with 5 degrees step; range from –45 (all to port) to +45 (all to starboard).
Last three variables will be the same for port side propeller, engines and helm.
Construction of the Bayesian belief network
The Bayesian network is acyclic directed graph in which nodes represent above described random variables and arcs represent direct probabilistic dependences among them (Fig. 3). The structure of the Bayesian network is graphical, qualitative illustration of the interactions among the set of variables, that is models. The Bayesian network also represents the quantitative relationships among the modeled variable. Numerically, it represents the joint probability distribution among them. This distribution is described efficiently by exploring the probabilistic independences among them. The structure of the directed graph can mimic the casual structure of the modeled domain, although it is not necessary. When the structure is casual, it gives a useful, modular insight into interactions among the variable.
First suggested structure of BN will be the simplest naive network. Nodes will be defined by described set of variables. Next the structure of the Bayesian belief network will be discovered by using for example Bayes Net Learner program [4].
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74 Scientific Journals 20(92)
Sets of data for BN will come from real time manoeuvres and will collect information for 4 categories of described variables. Then, the BN will be learned by another application for example BayesiaLab [5]. Verification of the model can be carried out by another set of data than were used for discovering and learning BN.
Fig. 3. The Bayesian network of draft moorings manoeuvres decision model
Rys. 3. Sieć bayerowska wstępnego modelu rozpoznawania decyzji manewrowych
Assumed that for construction of the BN model will be required NMEA data from 100 up to 120 m/f “Polonia” moorings manoeuvres at the port of Ystad. During collecting of the data it is necessary to confirm suggested methodology. By using non complete data with reduced precision and quantity of variables the transitional model should be constructed and tested. Bayesian network include only 11 less accuracy variables from data of 35 passages. For the testing set of data received not
less that 55,7% properly predicted variable at 95% confidence level. Application used for draft model was Belief Network PowerSoft [6]. The conclusion can be drawn that when will be used suggested and complete of 4 groups described variables it should receive the enough precision model.
Conclusion
It seems possible to model Captain’s manoeu-vres decision process by using Bayesian belief net-work. The nodes of the BN will be selected from set of variables represent navigational information signals. For successful research it is possible to adjust of variables discretization steps. Due to the same reason, another combination of variables can be taken into consideration.
Suggested model will represent real manoeu-vring decision in the real environment. That will be a progress in comparison with other models based on simulation of the manoeuvres.
References
1. GUCMA S.: Nawigacja Pilotażowa. Fundacja Promocji Przemysłu Okrętowego i Gospodarki Morskiej, Gdańsk 2004.
2. KORB K.B., NICHOLSON A.E.: Bayesian Artifical Intelli-gence, Chapman & Hall / CRC Press LLC, New York 2004.
3. KOWALSKI A.: Estimating Manoeuvres Safety Level of the Unity Line m/f “Polonia” Ferry at the Port of Ystad. Inter-national Conference Marine Navigation and Safety of Sea Transportation, TransNav’2009.
4. Bayes Net Learner. The Auton LabCarnegie Mellon Uni-versity, www.autonlab.org.
5. BayesiaLab Bayesia S.A., www.bayesia.com.
6. Belief Network PowerSoft. Jie Cheng. Department of Computing Science. University of Alberta. www.cs. ualberta.ca.
