This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).

The agile and safe transdomain in waves is a promising feature but the primary bottleneck of the hybrid aerial underwater vehicle (HAUV). In this article, the surfing algorithm is proposed for Nezha-mini, our predeveloped HAUV prototype, to search for the dynamic window facilitating takeoff in waves and avoiding hazardous waves. For the first time, the cross-domain window, i.e., the vehicle is at the wave crest and heading downstream, is characterized and defined through the vehicle-wave coupled dynamic model. The novel surfing algorithm consists of the gradient perceptron, time-limited momentum gradient search, heading server, and initial conditions. Nezha-mini senses, searches, and tracks the dynamic window in real-time, until the takeoff decisions are triggered. Numerical simulations and experiments in regular and irregular waves reveal the effectiveness of the algorithm. The vehicle maintains a healthy initial attitude and inaccessible wave disturbance during takeoff, thus alleviating the thrust distraction from stability recovery and uncertainty. The average transition time and energy cost are reduced by 59.2% and 26.1% compared with random takeoff cases, and the locomotion is smooth, graceful, and low-risk. The computation and cost are low as the algorithm only requires the basic flight controller and the data from the inertial measurement unit instead of the prior parameters of the HAUV and waves. In comparison with the adaptive robust controller, which resists wave disturbance directly, this article provides an enlightening strategy from the perspective of harnessing waves.

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 letter proposes a structurally simple, locally fixed-time stabilization method for first-order systems with input saturation by introducing a time-compensation coefficient. Unlike conventional anti-saturation compensation approaches, the proposed controller preserves the nominal fixed-time control structure while ensuring convergence within a bounded time independent of initial conditions. The key innovation lies in direct adaptation of the time-compensation coefficient, which is determined by using the relationship between the Lyapunov function and the convergence time of fixed-time stable system. Theoretical analysis confirms local fixed-time stability under input saturation, with the upper bound of the convergence time not changed. This method also shows the potential to be applied to high-order systems. Numerical simulations on a first-order system demonstrate that the proposed controller achieves the original convergence time despite saturation.

As emerging multirotor-type hybrid aerial underwater vehicles capable of both flight and underwater operations, numerous conceptual prototypes have been proposed in recent years. However, existing designs lack a balanced consideration of cross-medium stability, underwater maneuverability, and mode-switching reliability within a single configuration, and there remains no unified evaluation criterion for cross-domain performance. To compensate for the limitations of conventional optimization-based design, this work leverages key constraints identified through experiments and employ a multiobjective optimization approach to achieve a balanced design among these competing requirements. A lightweight and reliable foldable-arm system is further developed to support the implementation of the optimized configuration. In addition, through systematic integration of aerial propulsion bench test data with vehicle design parameters, we develop novel static quantitative metrics (cross-domain thrust margin@50%) to comprehensively assess overall design rationality. Finally, simulation and field tests show that the underwater damping of the final prototype has decreased by 55.2%. Compared with the previous generation, the cross-domain average power consumption is reduced by 68.9%.