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Ship Manoeuvring Research 2010–2025: From Hydrodynamics and Control to Digital Twins, AI and MASS

Mina Tadros, Myo Zin Aung, Panagiotis Louvros, Christos Pollalis, Amin Nazemian, Evangelos Boulougouris

Year
2025
Citations
2
Access
Open access

Abstract

Over the past fifteen years, ship manoeuvring has evolved from a highly specialised branch of marine hydrodynamics into a key enabler within multidisciplinary research, integrating seakeeping and intact stability, and paving the way for digital twins and autonomous maritime systems. The scope of this review is to examine the existing literature in a way that paves the way forward for integration with robotics, aerial and surface drones, digital-twin (DT) ecosystems, and other interconnected autonomous platforms. This paper reviews the published articles during this period, tracing the field’s progression from classical hydrodynamic models to intelligent, data-centric, and regulation-aware maritime systems. Drawing on a structured bibliometric dataset covering 2010–2025, this study organises the literature into interconnected themes spanning physics-based manoeuvring models, adaptive and predictive control, machine learning and digital-twin (DT) technologies, collision-avoidance and regulatory reasoning, environmental performance, and cooperative autonomy. The analysis reveals the transition from static empirical modelling toward hybrid physics, artificial intelligence (AI) frameworks capable of capturing nonlinear dynamics, uncertainty, and multi-vessel interactions. At the same time, this review highlights the growing influence of Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the Second-Generation Intact Stability Criteria, and emissions-reduction targets in shaping technical developments. While learning-enabled prediction, model predictive control (MPC)-based regulatory compliance, and real-time DT synchronisation show increasing maturity, this study identifies unresolved challenges, including domain shift, model interpretability, certification barriers, multi-agent safety guarantees, and DT divergence under sparse data. By mapping both demonstrated capabilities and conceptual frontiers, this review presents manoeuvring as a central pillar of future Maritime Autonomous Surface Ships (MASS) operations and sustainable shipping. The findings outline a research agenda toward integrated, explainable, and environmentally aligned manoeuvring intelligence that can support safe, efficient, and regulation-compliant autonomous maritime systems.

Keywords

Scope (computer science)SeakeepingDomain (mathematical analysis)Model predictive controlCertificationControl (management)Marine technologyCommand and control

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