Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships (en Inglés) - Chunhui Zhou;Yixiong He;Liang Huang

This Reprint spotlights safe ship maneuvering, efficient navigation and intelligent management-the core of maritime transport's safe, green and efficient development. It compiles cutting-edge tech-driven research to reshape intelligent shipping, delivering key insights into the field's latest advances. Focusing on maritime safety, it builds an intelligent, data-driven safety framework, featuring ontology model-based abnormal ship behavior detection, edge computing and lightweight AI for real-time target tracking and dynamic collision risk calculation for complex waters, forming an integrated safety support system. For ship motion prediction and autonomous control, it features AI + Control advances, including LSTM-based USV maneuver prediction and optimized DDPG algorithms for USV trajectory tracking, moving from model-driven to resilient data-driven self-learning control. It also includes ship group multi-agent collaboration, such as time-delay cooperative formation control and port traffic simulation, optimizing resources, cutting turnaround times and easing congestion. Systematically, this Reprint applies percolation theory to inland waterway network reliability analysis and uses historical AIS data to assess offshore wind farm impacts on maritime traffic, supporting marine spatial planning and shipping-energy synergy. It also explores emerging sustainable shipping topics: polar ice field ship maneuverability through AI-physical model integration, and alternative fuel applications for ship carbon reduction. These achievements support the high-quality development of intelligent shipping.