Artificial neural network controller for automatic ship berthing: challenges and opportunities

Abstract

Ship berthing is a complicated manoeuvring process that demands precise control of the speed and course of an under-actuated system. As berthing involves low speed running of a ship under environmental disturbances with reduced manoeuvrability, a professional ship handler often faces difficulties in controlling heading in such a situation. To bring automation in ship berthing, most of the researchers have agreed that Artificial Neural Network (ANN) plays a vital role as it has the ability to learn from human experience and replicate similar action in an unknown situation. However, we are still far away from implementing it for real berthing control as we do not have any self-fulfilling ANN controller yet, which can treat all the major issues relevant to a complicated ship berthing operation. Based on contemporary research findings, this paper, therefore, highlights four major challenges that have to be taken into account while proposing an ANN controller for ship berthing, and a comprehensive summary of how to deal with those. The first is how to provide consistent teaching data while training ANN controller to make it more robust; second is how to make the controller universal to do berthing in any port; third is how to tackle the wind disturbances while automation in progress; and the fourth is how to align a ship to the pier, which is the final stage of berthing.