The Comparison of Two Kinematic Motion Models for Autonomous Shipping Maneuvers

Abstract

Autonomous shipping with adequate decision support systems is widely considered as a high-potential development direction in the maritime industry in the upcoming years. Prediction technologies are one of the key components in these decision support systems and they usually require a large number of data sets to estimate vessel states. Certain vessel motion models are generally implemented with the above-mentioned prediction technologies to improve the accuracy and robustness of the estimated states. In contrast to wider research studies of different motion models for the applications of ground vehicles, the studies of appropriate motion models for maritime transport applications are still insufficient. Therefore, it is necessary to develop reliable motion models for vessels, and that can improve the decision supporting capabilities in future vessels, especially in autonomous shipping. In this paper, two kinematic motion models which can be used to estimate various vessel maneuvering states are examined and compared. In the current stage, the proposed models are used to investigate ship maneuvers produced by a ship bridge simulator. Two nonlinear filter algorithms combined with Monte Carlo-based simulation tests are applied to estimate the respective vessel states. In the conclusion, a comprehensive comparison of the estimation algorithms is presented with the estimated vessel states. Hence, this study provides robust and convenient estimation algorithms that can support autonomous shipping navigation in the future.