• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.304-308
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• 2007

This paper deals with ANN(Artificial Neural Networks) and its application to automatic ship berthing. Due to the characteristic of ship's manoeuvre comparing with other moving objects on land, 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 appropriate weights 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.

• International Journal of Naval Architecture and Ocean Engineering
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• 제6권2호
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• pp.347-360
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• 2014

The auto-berthing of a ship requires excellent control for safe accomplishment. Crabbing, which is the pure sway motion of a ship without surge velocity, can be used for this purpose. Crabbing is induced by a peculiar operation procedure known as the push-pull mode. When a ship is in the push-pull mode, an interacting force is induced by complex turbulent flow around the ship generated by the propellers and side thrusters. In this paper, three degrees of freedom equations of the motions of crabbing are derived. The equations are used to apply the adaptive backstepping control method to the auto-berthing controller of a cruise ship. The controller is capable of handling the system non-linearity and uncertainty of the berthing process. A control allocation algorithm for a ship equipped with two propellers and two side thrusters is also developed, the performance of which is validated by simulation of auto-berthing.

• 대한조선학회논문집
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• 제58권6호
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• pp.358-365
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• 2021

Crabbing motion is a pure sway motion with only sway velocity. The ship's crabbing motion is essential for an ideal berthing/unberthing process. The unberthing situation proceeds in sequential order such as crabbing motion section, pivoting section, and outer port section. For the berthing situation, the sequence has a reverse order: the inner port section, pivoting section, and crabbing motion section. In this paper, the berthing/unberthing data of the reference ship, Pukyong National University research ship "NARA", was analyzed to develop a sway velocity reference model. Several constraints were defined to derive the crabbing motion section during berthing/unberthing. The sway velocity reference model for the auto-berthing/unberthing was developed using the estimated sway velocity. A reproduction simulation of the ship was performed to compare the designed reference model and the reference ship data.

• 동력기계공학회지
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• 제10권4호
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• pp.139-146
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• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image processing performance in building an effective measurement system using cameras are described for automatically berthing and controlling the ship equipped with side thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image processing time of fourfold as compared with the typical template matching method.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2004년도 춘계학술발표회
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• pp.107-112
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• 2004

선박의 대형화 추세에 따른 항만 운영의 효율성 및 대형 선박의 접안 안전성을 향상시키기 위해서는 먼저 선체에 작용하는 접안에너지를 합리적으로 산출하여야 한다. 선박의 접안에너지는 방충재와 같은 항만시설물의 설계에 대한 허용 기준을 결정하는 변수임과 동시에 도선사 및 선박의 조선자에게는 예인선의 필요 마력이나 접안속도 둥을 결정하는데 있어 중요한 판단 요소로 활용된다. 본 연구에서는 현재 사용되고 있는 운동역학적인 방법을 토대로 한 접안에너지 산출 방법에 대해 유체역학적인 측면에서 문제를 제기하고, 부두 전면의 수심과 선박의 형상에 따라 변화하는 천수역 선체부가질량을 고려한 접안에너지의 산출 방법을 제안한다. 또한 천수역에서 선체에 작용하는 부가질량을 고려한 접안에너지 산출 방법을 사용하여 1600TEU급에서 12000TEU급까지의 컨테이너 선박을 대상으로 계통적으로 각각의 접안에너지를 계산하고, 현행의 접안에너지 산출방법과 비교 검토를 실시한다.

• 한국항해학회지
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• 제18권2호
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• pp.69-81
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model, but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics at low speed. In this paper, the authors propose a new berthing control system which can evaluate as closely as cap-tain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS-90 MK Ⅲ) and represent the ship motion characteristics internally. According to learning procedure, both FNN controllers can tune membership functions and identify fuzzy control rules automatically. The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• International Journal of Naval Architecture and Ocean Engineering
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• 제10권3호
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• pp.235-249
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• 2018

The Artificial Neural Network (ANN) model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports.

• 대한임베디드공학회논문지
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• 제15권2호
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• pp.71-78
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• 2020

This paper proposes vision sensors and deep learning-based around view monitoring system for ship berthing. Ship berthing to the port requires precise relative position and relative speed information between the mooring facility and the ship. For ships of Handysize or higher, the vesselships must be docked with the help of pilots and tugboats. In the case of ships handling dangerous cargo, tug boats push the ship and dock it in the port, using the distance and velocity information receiving from the berthing aid system (BAS). However, the existing BAS is very expensive and there is a limit on the size of the vessel that can be measured. Also, there is a limitation that it is difficult to measure distance and speed when there are obstacles near the port. This paper proposes a relative distance and speed estimation system that can be used as a ship berthing assist system. The proposed system is verified by comparing the performance with the existing laser-based distance and speed measurement system through the field tests at the actual port.

• 한국해양공학회지
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• 제37권3호
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• pp.122-128
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• 2023

Autonomous berthing is a crucial technology for autonomous ships, requiring optimal trajectory planning to prevent collisions and minimize time and control efforts. This paper presents a two-phase, two-point boundary value problem (TPBVP) strategy for creating an optimal berthing trajectory for a twin-propeller, twin-rudder ship with autonomous berthing capabilities. The process is divided into two phases: the approach and the terminal. Tunnel thruster use is limited during the approach but fully employed during the terminal phase. This strategy permits concurrent optimization of the total trajectory duration, individual phase trajectories, and phase transition time. The efficacy of the proposed method is validated through two simulations. The first explores a scenario with phase transition, and the second generates a trajectory relying solely on the approach phase. The results affirm our algorithm's effectiveness in deciding transition necessity, identifying optimal transition timing, and optimizing the trajectory accordingly. The proposed two-phase TPBVP approach holds significant implications for advancements in autonomous ship navigation, enhancing safety and efficiency in berthing operations.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1994년도 춘계학술발표회
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• pp.55-67
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics ar low speed. In this paper the authors propose a new berthing control system which can evaluate as closely as captain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's decision-making by using the FNN(Fuzzy neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS90 MK III) and represent the ship motion characteristics internally According to learning procedure both FNN controllers can tune membership functions and identify fuzzy control rules automatically The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.