Energy management is a critical and challenging factor required for efficient and safe operation of underwater gliders (UGs), and the energy consumption model (ECM) is indispensable. In this paper, a more complete ECM of UGs is established, which considers ocean currents, seawater density variation, deformation of the pressure hull, and asymmetry of gliding motion during descending and ascending. Sea trial data are used to make a comparison between ECMs with and without the consideration of ocean currents, and the results prove that the ECM that considers the currents has a significantly higher accuracy. Then, the relationship between energy consumption and multiple parameters, including gliding velocity relative to the current, absolute gliding angle, and diving depth, is revealed. Finally, a simple example is considered to illustrate the effects of the depth-averaged current on the energy consumption.

Accurate quantitative plankton observation is significant for biogeochemistry and environmental monitoring. However, current observation equipment is mostly shipborne, and there is a lack of long-term, large-scale, and low-cost methods for plankton observation. This paper proposes a solution to investigate plankton using a Seascan holographic camera equipped with a “Petrel-II” underwater glider for a longer time sequence and at a larger scale. Aiming at the new challenges of low efficiency and low accuracy of holographic image processing after integrating holographic imaging systems and underwater gliders, a novel plankton data analysis method applicable to Digital Holographic Underwater Gliders (DHUG) is proposed. The algorithm has the following features: (1) high efficiency: the algorithm breaks the traditional hologram information extraction order, focusing only on the key regions in the hologram and minimizing the redundant computation; (2) high accuracy: applying the Sobel variance algorithm to the plankton in the hologram to focus the plane extraction significantly improves the focus accuracy; and (3) high degree of automation: by integrating a convolutional neural network, the algorithm achieves a fully automated analysis of the observed data. A sea test in the South China Sea verified that the proposed algorithm could greatly improve the problems of severe plankton segmentation and the low focusing accuracy of traditional information extraction algorithms. It also proved that the DHUG plankton survey has great potential.

Mesoscale eddies play an important role in regulating the global ocean ecosystem and climate variability. However, few studies have been found to focus on the survey of the underwater gliders (UGs) motion performance inside mesoscale eddies. The dynamic model of an UG considering the eddy density is established to predict its motion performance inside an eddy. Ignoring the effect of vertical velocity inside the eddy on the motion of UG, the simulation results and experimental data are compared to verify the derived model. From the analysis of the motion performance, the vertical velocity is larger at 400∼940 m depth than that at a depth of 0∼400 m in the ascent. Considering the vertical structures of parameters within eddies, the climbing profiles are chosen as the available samples to capture an eddy better. The larger error caused by the eddy density mainly occurs near the depth of the thermocline. Moreover, there is a stronger influence of eddy density on the motion performance of the UG in the ascent than that in the descent. The results show the differences in the effect of the mesoscale eddy density on the motion performance of “Petrel II” UG in the descent and ascent, and they provide a sampling suggestion for the application of UGs in the mesoscale eddy observation.

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

To address the issue that current underwater glider deployments for in situ observation of the 3D structure of mesoscale eddies are largely guided by empirical knowledge rather than a systematic quantitative evaluation framework, this paper, based on a synthesized anticyclonic eddy field in the northern South China Sea, systematically evaluates the reconstruction performance of three array topologies (parallel, grid, and radial) through numerical simulations, and proposes a comprehensive metric integrating coverage, error, and correlation coefficient to assess the reconstruction performance. Results show that the full-depth optimal configuration requires at least 7 units for the parallel and grid arrays, and 8 units for the radial array; further stratified simulations quantitatively reveal configuration discrepancies between the shallow layer (above 200 m) and the deep layer (below 200 m), and determine the optimal glider number and array configuration. A field experiment conducted by Tianjin University in August 2017, involving 12 gliders in the northern South China Sea, verifies the above simulation results. These results aid in designing glider arrays for in situ observation of the 3D structure of mesoscale eddies.