A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

Abstract

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.