About this Research Topic
Ocean observation and exploration have long been pivotal for the advancement of marine science, climate change study, and resource utilization. However, traditional oceanographic methodologies that involve crewed vessels or satellite data can be limited by factors like high operational costs, potential human risk, temporal and spatial resolution limitations. Autonomous Ships (ASs), also known as Unmanned Surface Vessels (USVs), have emerged as a promising alternative with their high endurance, lower operating costs, and the capability of venturing into hazardous or hard-to-reach environments.
USVs or ASs are increasingly being utilized for oceanographic research tasks such as data collection, seafloor mapping, environmental monitoring, and marine life surveys. Equipped with a variety of sensors and devices, these autonomous ships can perform observations and gather crucial oceanic data over large geographical scales and extended time frames, providing a wealth of valuable information for marine scientists.
This special issue seeks to explore the latest developments in the role of ASs in enhancing our understanding of the marine environment, with a focus on their potential to increase the efficiency and accuracy of oceanic observation. We aim to explore how the integration of advanced technologies in ASs, such as Artificial Intelligence (AI) and Machine Learning (ML), can optimize data collection and analysis, potentially revolutionizing marine and ocean science research.
Topics of interest include, but are not limited to
1. ML/AI techniques in ASs for ocean observation
2. Design of ASs or/and USVs for ocean observation
3. Perception, decision-making, path planning, and control of unmanned autonomous ocean observation platform
4. Collaborative strategies for multi-unmanned ocean observation systems
6. Safety regulatory aspects in ASs for ocean observation
7. Case studies and trial instances of ASs from the real world
8. Software and hardware architectures for unmanned autonomous ocean observation platform
9. E-navigation and observation data storage, transmission, and analysis
10. Ocean ecological environment remote sensing and monitoring
11. Ocean Event Prediction and Early Warning Technology
12. Ocean numerical simulation
USVs or ASs are increasingly being utilized for oceanographic research tasks such as data collection, seafloor mapping, environmental monitoring, and marine life surveys. Equipped with a variety of sensors and devices, these autonomous ships can perform observations and gather crucial oceanic data over large geographical scales and extended time frames, providing a wealth of valuable information for marine scientists.
This special issue seeks to explore the latest developments in the role of ASs in enhancing our understanding of the marine environment, with a focus on their potential to increase the efficiency and accuracy of oceanic observation. We aim to explore how the integration of advanced technologies in ASs, such as Artificial Intelligence (AI) and Machine Learning (ML), can optimize data collection and analysis, potentially revolutionizing marine and ocean science research.
Topics of interest include, but are not limited to
1. ML/AI techniques in ASs for ocean observation
2. Design of ASs or/and USVs for ocean observation
3. Perception, decision-making, path planning, and control of unmanned autonomous ocean observation platform
4. Collaborative strategies for multi-unmanned ocean observation systems
6. Safety regulatory aspects in ASs for ocean observation
7. Case studies and trial instances of ASs from the real world
8. Software and hardware architectures for unmanned autonomous ocean observation platform
9. E-navigation and observation data storage, transmission, and analysis
10. Ocean ecological environment remote sensing and monitoring
11. Ocean Event Prediction and Early Warning Technology
12. Ocean numerical simulation
Keywords: Ocean observation, unmanned surface vehicles, autonomous ships, unmanned autonomous ocean observation platform, e-navigation
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