As marine exploration becomes increasingly important, marine robots have been extensively studied in recent years. Despite some well-designed robots have already achieved to various successful missions, most existing robots struggle to adapt to diverse demands or tasks due to their fixed structure and complexity of the marine environment. To address these challenges, we present a novel reconfigurable marine robot named Sea-U-Whale. This system can dynamically adjust its actuator configuration in the marine environment, providing superior environmental adaptability, maneuverability, and ver-satile mobility. Considering the demands of unmanned ocean exploration, an active reconfiguration mechanism and three distinct vehicle modes are designed for optimal actuation in various marine scenarios. The multi-modal mobility of our system and its robust performance have been validated through extensive field tests and water tank experiments, demonstrating its potential in handling a wide range of mission profiles.

Accurately predicting ocean subsurface temperature is vital for advancing ocean and climate research, particularly given the sparse and costly nature of subsurface observations. This study introduces sparse-to-dense prediction of ocean subsurface temperature using multi-level spatiotemporal (ST) information fusion. The framework integrates interpretable ST decoupling, adaptive feature updating, and sparse-to-dense information fusion modules to address the challenge of sparse observations and ever-evolving dynamic environments. Comprehensive experiments focused on the Pacific demonstrate the superiority of the proposed methodology over peer methods. The proposed methodology achieves high-resolution predictions with a root mean square error of 0.2230, accuracy of 0.9846, and point-wise prediction errors below 0.5°C under 10% online random sparse observations (ORSO). Analyses of spatial and temporal temperature dynamics reveal long-term warming trends in the Pacific, including a temperature rise of up to 2.8°C at -100 m in low-latitude regions over the past 40 years, and identify the latitudinal slope of thermocline dynamics. This study advances the understanding of multi-scale thermal processes and variability in the Pacific, demonstrating the potential of application in climate studies, marine resource management, and environmental monitoring.

Autonomous Marine Vehicles (AMVs) have been widely used in many critical tasks such as surveillance, patrolling, marine environment monitoring, and hydrographic surveying. However, most typical AMVs cannot meet the diverse demands of different marine tasks. In this article, we design a new type of remote-controlled hydrofoil marine vehicle, named Sea-U-Foil, which is suitable for different marine scenarios. Sea-U-Foil features three distinct locomotion modes, displacement mode, foilborne mode, and submarine mode, which enable the platform flexible mobility, high-speed and high-load capacities, and superior concealment. Specifically, the submarine mode makes Sea-U-Foil unique among previous studies. In addition, the performance of Sea-U-Foil in foilborne mode outperforms those of most current unmanned surface vehicles (USVs) in terms of speed and payload. To the best of our knowledge, we are the first to introduce a new type of AMV that can work in displacement mode, foilborne mode, and submarine mode. We elaborate on the design principles and methodologies of Sea-U-Foil first, then validate the effectiveness of its tri-modal locomotion through extensive experiments.

This article develops and tests an algorithm of spatial congruence based on geometric congruity of two spatial areal objects in the Euclidean plane. Spatial congruence is defined and thus evaluated as an increasing continuous function of congruity in the position, orientation, size, and shape of spatial objects, dependent upon scaling, translation, and rotation. Expansion-based geometric matching is used to seek the best match between the two objects of interest for the examination and differentiation of the congruence effects of their spatial and geometric properties, while the expansion-inflated size effect is deflated or filtered out accordingly. The use of both expansion and deflation not only allows for a trade-off between size and position, both of which are found substitutable for each other in congruence measurement, but also enables the congruence algorithm to be highly sensitive to differences or changes in these properties. Three geographical objects (the states of Texas, Mississippi, and Louisiana) are used to show how trade-offs among the four properties are manipulated by the congruence algorithm in a geographic information system (GIS) environment, ArcGIS®. In addition, three regular geometric objects are used to demonstrate how the congruence algorithm is sensitive even to small changes in each of the four properties of objects. The results show that the proposed congruence algorithm is capable of quantifying the extent of congruity between two spatial objects regardless of how they are related as described in topological relations.