Airborne-launched AUVs withstand great fluid impact force at the early stage when entering the water, which may cause damage to their structure and inner components in severe cases. Due to their large volume and mass, the major challenge involved in conducting experiments to measure the water entry impacts on real-life AUVs is the high demand for the experimental devices, finding a suitable site, and the cost of the experiments. Using a gas gun as launching device, water entry experiments using a full-size AUV model are conducted under various conditions. The axial and radial force changes that occur during the water entry process are obtained, and some accompanied phenomena such as cavitation and turnover under different water entry conditions are observed. Computational fluid dynamics (CFD) is used to simulate the water entry process of airborne-launched AUVs. The simulation results fit well with the experimental data, the latter of which show that both the water entry velocity and entry angle have a great influence on the impact load during the water entry process. These data can provide valuable reference information for AUV structure design and launch condition selection.

Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.

According to the characteristics of AUV movement, a fuzzy sliding mode controller was designed, in which fuzzy rules were adopted to estimate the switching gain to eliminate disturbance terms and reduce chattering. The six-degree-of-freedom model of AUV was simplified and longitudinal motion equations were established on the basis of previous research. The influences of first-order wave force and torque were taken into consideration. The REMUS was selected to simulate the control effects of conventional sliding mode controller and fuzzy sliding mode controller. Simulation results show that the fuzzy sliding mode controller can meet the requirements and has higher precision and stronger antijamming performances compared with conventional sliding mode controller.

The water exit of a morphing cross-media vehicle is a nonlinear, multi-coupled, physical process with strong disturbance. It severely affects the subsequent flight of the vehicle after leaving the water. Because of the short duration, it requires very fast response time from the controller. To overcome the shortcomings of existing research on the water exit process of morphing cross-media vehicles, a possible solution of uncontrolled water exit and controlled takeoff is proposed. A water exit dynamic model is established after properly simplifying the complexity and unpredictability of the underwater environment, in consideration of changing variables, such as buoyancy, wetted area, and added mass. A numerical simulation is conducted to prove the validity of the model. Furthermore, an improved sliding-mode controller is designed to ensure that the cross-media vehicle reaches the target conditions after leaving the water and to prevent the vehicle falling back into the water. Simulation results demonstrate the reasonability and feasibility of the proposed solution. These conclusions can provide valuable reference information for solving control problems in morphing cross-media vehicles.

This paper addresses the challenge of simultaneous arrival for UAV clusters and proposes a route planning method based on an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III). Initially, the paper defines the simultaneous arrival problem and formulates the corresponding mathematical model, considering the complexity of multi-objective optimization in UAV clusters. A novel path generation framework is introduced, which incorporates multiple optimization objectives—such as time coordination, threat mitigation, and resource consumption—aimed at improving flight safety, efficiency, and resource management. To enhance the algorithm’s search performance, a hybrid approach combining the Artificial Bee Colony (ABC) algorithm with NSGA-III is proposed. This improved NSGA-III strategy overcomes the limitations of the original algorithm in managing complex constraints and multi-objective optimization problems, resulting in significant improvements in search accuracy and convergence speed. Finally, the performance of the improved algorithm is evaluated through simulations and compared with traditional methods. The results show that the proposed approach optimizes flight time, reduces resource consumption, and effectively mitigates threats, all while ensuring the simultaneous arrival of UAV clusters.