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Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey

Zhang, X, Wang, C, Jiang, L, An, L and Yang, R (2021) Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey. Ocean Engineering, 235. ISSN 0029-8018

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The rapid development of artificial intelligence significantly promotes collision-avoidance navigation of maritime autonomous surface ships (MASS), which in turn provides prominent services in maritime environments and enlarges the opportunity for coordinated and interconnected operations. Clearly, full autonomy of the collision-avoidance navigation for the MASS in complex environments still faces huge challenges and highly requires persistent innovations. First, we survey relevant guidance of the International Maritime Organization (IMO) and industry code of each country on MASS. Then, major advances in MASS industry R&D, and collision-avoidance navigation technologies, are thoroughly overviewed, from academic to industrial sides. Moreover, compositions of collision-avoidance navigation, brain-inspired cognitive navigation, and e-navigation technologies are analyzed to clarify the mechanism and principles efficiently systematically in typical maritime environments, whereby trends in maritime collision-avoidance navigation systems are highlighted. Finally, considering a general study of existing collision avoidance and action planning technologies, it is pointed out that collision-free navigation would significantly benefit the integration of MASS autonomy in various maritime scenarios.

Item Type: Article
Uncontrolled Keywords: 0405 Oceanography; 0905 Civil Engineering; 0911 Maritime Engineering; Civil Engineering
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HE Transportation and Communications
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Engineering
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 06 Feb 2023 11:52
Last Modified: 06 Feb 2023 12:00
DOI or ID number: 10.1016/j.oceaneng.2021.109380
URI: https://researchonline.ljmu.ac.uk/id/eprint/18414
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