An Outlook on the Future Marine Traffic Management System for Autonomous Ships

Abstract

In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing/actuation infrastructure and the AI algorithms' results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships' navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.

Challenges for the Education and Training of Seafarers in the Context of Autonomous Shipping: Bibliometric Analysis and Systematic Literature Review

“…These skills can be enhanced through the GTGP training model, which combines actual onboard training, group work and presentations to prepare maritime personnel to master AI and not just rely on it. The system proposed by Martelli [66], combines augmented and virtual reality with machine learning for autonomous surface ships. It holds significant potential for dynamic collaboration in the maritime sector by addressing the challenges of using AI algorithms through improved information gathering and visualization.…”

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

“…In [18], Martelli et al present an intelligent autonomous ship traffic system, which consists of the following layers: the identification of the navigation situation and collision avoidance; the coordination of the movement of many ships, while taking into account environmental conditions; and the management of ship navigation in a crowded water area.…”

Multi-Parametric Methodology for the Feasibility Assessment of Alternative-Fuelled Ships

“…Maloberti and Zaccone [17] developed an optimization algorithm to minimize the GHG emission of a hybrid system installed on board a touristic ferry. Additionally, the researchers have begun to study autonomous surface vessels driving this sector towards more energy-efficient ships with minor operational expenditures [18].…”