This paper proposes a novel hybrid underwater vehicle, the Smartfloat, which integrates the concept of buoyancy-driven underwater gliders and conventional profiling floats. The vehicle is presented to address the challenges in ocean monitoring, such as the multidisciplinary observations of long-range transport of the mesoscale features with high spatial-temporal resolution. The vehicle combines the mechanisms of Argo profiling float and the underwater glider, with the application of a special designed attitude control system and the ingenious general arrangement. The vehicle can switch the operating mode between drifting mode and gliding mode. Such a multimodal vehicle would merge the benefits of making measurements in a very energy-efficient way when drifting with the currents in Argo mode, and operating as an underwater glider in glider mode when it is needed to cross the ocean eddies, ocean fronts, filaments, or some stirring regions. In this paper, a proof-of-concept platform including its concept of operations, the main components and subsystems design, and the correlative mathematical analysis is introduced in detail. Experiments in field trials are presented to characterize and illustrate each mode of operation and repeated mode transitions. The results demonstrate the feasibility and the good performance of the proposed vehicle.

The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value.

Multifarious and continuous oceanographic observation data is of considerable significance to oceanographic research and military application. In this paper, a novel oceanographic observation method with dual-modal underwater vehicle able to switch between the Argo and Glider modes is proposed and is studied experimentally in the South China Sea. Using the Newton–Euler formulation, a complete dynamic model considering the variation in gravity/buoyancy due to mode switching is thus established. Then, based on Lyapunov stability criterion, a nonlinear adaptive inverse controller with multiple inputs and multiple outputs is developed which enables the observation platform to freely switch between various modes. By analyzing the numerical and experimental data of a single mode, the results demonstrate that the proposed method can supplement the observation data and improve the accuracy of oceanographic data. Finally, based on experimental data acquired by the dual-modal underwater vehicle in the South China Sea, three-dimensional regional oceanic temperature and salinity fields are constructed with the spatio-temporal Kriging method, and feature surfaces are extracted from the oceanic fields. Furthermore, the proposed method can be applied in other stirring dynamic regions, such as ocean eddies, ocean fronts, filaments, etc.

This letter proposes a novel conceptual multimodal underwater vehicle, named “Swift”, which is capable of dexterous attitude transition for multiple operation modes. Aside from being competent to adjust pitch and roll angles in the range of −90 $^{\circ }$ to 90 $^{\circ }$ , it can achieve in-situ heading angle maneuvering adjustment with nearly 150 $^{\circ }$ in cramped space, without any assistance of external driving mechanisms such as propeller or rudder. Ingenious configuration, design principles, and maneuvering control sequences of actuators are proposed for innovative attitude transition. Other characteristics, such as the shape and internal drive structures, are identical to classic underwater gliders without further upgrading, which offers great portability and extensibility. The description of the transient behavior during agile attitude transitions, particularly the effective estimation of in-situ heading angle adjustment, is greatly facilitated by transition dynamics with modified hydrodynamic compositions. The consistent results of dynamics simulation and various experiments demonstrate the performance of multi-mode locomotion and promote the transition characterization of the prototype.

This paper combines the piecewise Cubic Hermite (CH) interpolation algorithm and the weighted least square support vector machine (WLS-SVM) to improve identification accuracy for marine crafts built based on the characteristic model. The characteristic model is first used to describe the heading dynamics of marine crafts and is a superior model to the traditional response model in both accuracy and complexity. Especially in order to improve identification accuracy, a CH-based data preprocessing strategy is utilized to densify and smooth data for further accurate identification. Subsequently, the combination of the linear kernel function and the Gaussian kernel function is introduced in the conventional WLS-SVM method, which renders global and local performance improvements compared with the conventional WLS-SVM method. Finally, informative maneuvers composed of Zigzag and Sine are carried out to test the performance of the improved identification method. Compared to the conventional LS-SVM method based on the response model, the root mean square error of the proposed CH-MK-WLS-SVM method based on the characteristic model is reduced by an order of magnitude in the presence of sensor noise.