Artificial neural network controller for automatic ship berthing using head-up coordinate system

Im, Nam-Kyun; Nguyen, Van-Suong

doi:10.1016/j.ijnaoe.2017.08.003

Cited by 72 publications

(53 citation statements)

References 4 publications

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“…To automatize the ship berthing process in future, many studies have been performed to develop this system. In researches of [1,3,[5][6][7]9], neural network controllers were proposed to automatically maneuver the ship by changing the ship's rudder and propeller. The principle of those controllers is to mimic actions which are trained by teaching data in advance.…”

Section: The Concept Of Automatic Berthing Support System For Shipmentioning

confidence: 99%

“…In the situation of significant disturbance due to strong wind, a PD based controller was added to keep ship in imaginary line instead of ANN model. Recently, the authors [5] proposed an ANN controller based on head-up coordinate system, this controller is trained by teaching data in an original port and then the ship is controlled automatically into the berth in different ports without retraining the ANN structure. This controller has the advantage of being time-saving, cost-saving and simple structure.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with the head-up coordinate system based controller, the proposed controller in this research is more confident and it can be applied for unmanned ship in future. Although, the above mentioned researches [1][2][3][4][5][6] contributed the significant results to ship berthing, they only focused on how to maneuver automatically the ship from fair-way area to wharf area but not wharf position.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Ship Berthing Based on Fuzzy Logic

Nguyen

2019

IJFIS

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Because the maneuverability of ship at low speed is reduced significantly, ship berthing is one of most difficult duties that ship master usually has to face as arriving at the port. In order to make sure the safe accomplishment of ship berthing, all steps need to be performed properly. In fact of maritime safety, the tugboats and thrusters are commonly utilized to support crabbing motion of ship in final stage of berthing process before mooring. In addition, because of the nonlinear of ship dynamic on berthing, theories of artificial intelligence are suitable to mimic the actions of ship master in controlling ship berthing automatically. In this article, a support system is proposed to bring ship into berth automatically after using the maneuvering control system. Three fuzzy controllers are established for different tasks of the berthing algorithm. The first controller is designed to control the movement of ship in longitudinal direction toward to the normal of wharf with the ship's propeller, while the task of second controller is to stabilize the relative bearing error with tugboat. The final controller is responsible for bring the ship into wharf according to crabbing motion with bow thruster and tugboat simultaneously. Finally, numerical simulations are carried out to validate the performance of the proposed system. The results show that the berthing support system has good performance for ship.

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Section: The Concept Of Automatic Berthing Support System For Shipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Ship Berthing Based on Fuzzy Logic

Nguyen

2019

IJFIS

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“…To train the controller, the states, consisting of (D/L, H/L, θ, u 1 , v 1 , r 1 , rps, δ ord ) are saved and use later. The data of the ship was created in the original port as follows: (8, 6, 250, 1.5, 0, 0, 0.75, 0), (8,6, 210, 1.5, 0, 0, 0.75, 0), (8,4, 220, 1.5, 0, 0, 0.75, 0), (8,4, 250, 1.5, 0, 0, 0.75, 0), (8, 2, 240, 1.5, 0, 0, 0.75, 0), and (8, 2, 270, 1.5, 0, 0, 0.75, 0).…”

Section: Teaching Data Creation and Training The Neural Network In Ormentioning

confidence: 99%

“…In [8], the authors showed that existing ANN controllers could only control the ship into berth in a certain port. On arriving at new ports, either these controllers must be retrained by new teaching data or the ship has to install different controllers for ports, respectively.…”

Section: Introductionmentioning

confidence: 99%

Development of Automatic Ship Berthing System Using Artificial Neural Network and Distance Measurement System

Nguyen

2018

IJFIS

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In the studies on an berthing control of ship, an artificial neural network (ANN) model is commonly employed as the main controller to control the rudder and the propeller. The existing ANN controllers that use the parameters consisting of the ship position and the ship heading as inputs cannot be applied to control automatically the ship into berth in different ports. To deal with this problem, the parameters, such as relative bearing and distance from ship to berth calculated by radar can be used as inputs for the controller. However, the calculation of these factors is not accurate because some errors arise on using radar for berthing process. This leads to the lack of confidence in ship berthing system using the parameters determined by radar. In this research, the neural network based-automatic berthing system is developed for ship by using the parameters which are measured by distance measurement system. By this proposed system, the ship is brought automatically into berth in different ports without retraining the neural network. In addition, this system guarantees that the parameters used for inputs of the neural network is measured exactly and continually. To validate the proposed algorithm, numerical simulations are carried out to two imaginary ports and a real port, and result showed the good performance of the proposed system for automatic ship berthing.

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Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

Han¹,

Park²,

Park³

et al. 2023

Journal of Field Robotics

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This study introduces an advanced autonomous navigation algorithm for unmanned surface vehicle (USV) operations in complex coastal traffic areas. To facilitate the undertaking of tests for innovative technologies, the Korean government has designated regulation‐free special zones, thereby enabling field tests for USV demonstrations without an onboard safety person on real manned‐ship navigation routes. To enable real‐world USV operation without onboard safety person, an existing autonomous navigation algorithm is extended to achieve sufficiently reliable and robust USV operation. Accordingly, berthing and unberthing algorithms are newly developed for unmanned operation, and enhanced situational awareness algorithms are applied to detect all types of obstacles, including small‐sized floating objects spread over a wide range, such as a fish farm. In particular, the Korean intelligent maritime transportation service, called e‐Navigation, is incorporated into the autonomous navigation algorithms to overcome the limitations of vehicle autonomy based on onboard sensors. To demonstrate the capability of fully unmanned operation in real marine traffic scenarios, long‐distance navigation (54 km on a single voyage) was conducted by running several missions, including navigation in complex fish farm areas, maritime surveys, collision avoidance, navigation on real traffic routes, and emergency response. The extended navigation algorithms and results of the field tests are presented and discussed in this paper.

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Artificial neural network controller for automatic ship berthing using head-up coordinate system

Cited by 72 publications

References 4 publications

Automatic Ship Berthing Based on Fuzzy Logic

Automatic Ship Berthing Based on Fuzzy Logic

Development of Automatic Ship Berthing System Using Artificial Neural Network and Distance Measurement System

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

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