AI is utilized in the marine industry for applications such as autonomous navigation, vessel monitoring, and marine resource management. It employs machine learning algorithms and sensor data analysis to enhance safety, optimize operations, and facilitate sustainable practices in maritime transportation, fisheries, and offshore activities.
Researchers have conducted a comprehensive review of the offshore wind energy industry, emphasizing the role of machine learning (ML) and artificial intelligence (AI) in addressing challenges related to turbine size, efficiency, environmental impact, and deep-water deployment. ML applications include climate forecasting, environmental impact assessment, wind farm optimization, and more.
Researchers have introduced a novel decision support system utilizing fuzzy logic to improve collision avoidance in multi-vessel situations at sea. By integrating artificial intelligence and COLREG rules, the system identifies the most dangerous vessel and calculates collision avoidance maneuvers, demonstrating promise in two-ship scenarios but highlighting the need for further research in high-traffic areas.
Researchers have developed a cutting-edge ship detection and tracking model for inland waterways, addressing data scarcity issues. Leveraging few-shot learning and innovative transfer learning techniques, this model achieves remarkable accuracy, promising advancements in maritime safety and monitoring systems.
Researchers introduce a pioneering approach using deep reinforcement learning (RL) to enhance marine ranching's efficiency and resilience against disasters. This method, showcased in Energies, employs AI algorithms to optimize decision-making, create environmental models, and simulate disaster scenarios in marine ranching, contributing to sustainable fisheries management and disaster preparedness.
Researchers provide an in-depth analysis of cutting-edge path planning algorithms for unmanned surface vehicles (USVs). As USVs gain prominence in maritime applications, including transport, monitoring, and defense, path planning becomes vital for autonomous navigation. The review covers global and local path planning methods, hazard avoidance techniques, and multi-USV cluster coordination.
Researchers propose TwinPort, a cutting-edge architecture that combines digital twin technology and drone-assisted data collection to achieve precise ship maneuvering in congested ports. The approach incorporates a recommendation engine to optimize navigation during the docking process, leading to enhanced efficiency, reduced fuel consumption, and minimized environmental impact in smart seaports.
Researchers explore the advancements in Fault-Tolerant Control (FTC) technology for Autonomous Underwater Vehicles (AUVs) in a review, enhancing their reliability and robustness for underwater missions, marine exploration, environmental monitoring, and offshore industries.
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