RESEARCH

We conduct fundamental engineering research and core technology development focused on achieving higher precision, automation/labor-saving, higher efficiency, and miniaturization to realize oceanographic survey and deep-sea exploration systems capable of operating across vast areas and at extreme depths. In particular, we promote R&D that contributes to the development of communication and positioning network—applicable to operation of multiple vehicles—as well as the automation of exploration system.

To advance exploration capabilities at great depths, we will strengthen AI-driven exploration and autonomous behaviors, develop dynamics analysis technologies for exploration systems, and advance cooperative technologies for multiple vehicles. Furthermore, we will develop diverse exploration functions and technical systems characterized by high extensibility. Regarding the advancement of ocean observation capabilities, we aim to achieve more accessible, long-term, and high-frequency monitoring of the marine environment.

Recognizing the agility limitation of operating survey vehicles with traditional manned vessels due to personnel and travel time requirements, we are developing technologies for an agile survey system capable of being mounted on a labor-saving transport system.

Our goal is to develop a comprehensive ocean monitoring system for the marine environment. To address the lack of continuous, integrated observation and analysis technologies, we will combine automated data acquisition with computer models to detect marine environmental changes. Furthermore, we will use continuous acoustic monitoring to classify sounds and analyze the movement patterns of sound objects passing through the ocean.

Regarding seasonal meteorological and oceanographic conditions that significantly impact the safe offshore operation of ships, and recognizing that conventional forecasting technologies—including those for extreme events such as typhoons—lack sufficient resolution and accuracy, we will develop a system that enables high-precision, high-resolution seasonal forecasts of meteorological and oceanographic conditions, including extreme events, by improving the efficiency and speed of large-scale computations and effectively integrating them with observation data for validation and assimilation.