Abstract Machine Learning (ML) techniques have gained significant traction as a means of
improving the autonomy of marine vehicles over the last few years. This article surveys the …

A majority of marine accidents that occur can be attributed to errors in human decisions.
Through automation, the occurrence of such incidents can be minimized. Therefore …

Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and
more efficient mode of transportation and a potential solution to reduce the workload of …

Automatic collision avoidance decision making for vessels is a critical challenge in the
development of autonomous ships and has become a central point of research in the …

Ship collision avoidance is a complex process that is influenced by numerous factors. In this
study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for …

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in
their mission execution integrity. Yet, these thrusters directly interact with marine …

This research introduces a two-stage deep reinforcement learning approach for the
cooperative path planning of unmanned surface vehicles (USVs). The method is designed to …

The debonding defects in building façades pose a serious threat to the safety of residents. In
this paper, a two-stage quantitative network for debonding defect identification quickly and …

Autonomous ships are expected to extensively rely on perception sensors for situation
awareness and safety during challenging operations, such as reactive collision avoidance …

This paper proposes a spatial–temporal recurrent neural network architecture for deep Q-
networks that can be used to steer an autonomous ship. The network design makes it …