Autonomous maneuvering decisions of unmanned aerial vehicle (UAV) in short-range air combat remain a challenging research topic, and a decision method based on an improved deep deterministic policy gradient (DDPG) is proposed. First, the problem model is improved from the perspective of energy–air combat, and a decision model with engine thrust, angle of attack, and roll angle as control variables is established. The normal and tangential overloads are determined by these control variables, and the decision is constrained by the flight stability and threshold range. Subsequently, the decision learning algorithm of the maneuver command is designed based on the DDPG framework. According to the energy air combat, speed is introduced into the return function in some states to make the return value more in line with reality. In view of the slow learning speed of the DDPG algorithm, the winning rate is introduced into the $\varepsilon $ -greedy strategy to adjust the exploration and application probabilities in real time. In view of the decrease in computational efficiency caused by the large amount of empirical data, a similar empirical exclusion was carried out based on the vector distance. The simulation results show that the DDPG-based algorithm realizes autonomous decisions of engine thrust, roll angle, and attack angle under constraints, and the comparative simulation shows that the improvement measures are effective.

Hybrid aerial underwater vehicles (HAUV) are newly borne vehicle concepts, which could fly in the air, navigate underwater, and cross the air-water surface repeatedly. Although there are many problems to be solved, the advanced concept, which combines the integrated multidomain locomotion of both water and air mediums is worth exploring. This paper presents the water–air trans-media status of the HAUV from the perspective of the configuration and trans-media control. It shows that the multi-rotor HAUV is relatively mature and has achieved a stable water–air trans-media process repeatedly. The morphing HAUV is still in its exploration stage, and has achieved partial success.

When Unmanned Aerial Vehicles (UAVs) are used in search and rescue operations, electro-optical (EO) devices are usually used as the detection equipment, and area coverage is used as the main search method. However, the sector scanning mode of EO puts forward higher requirements for task parameter planning. First, to ensure there is no missing coverage, a method to determine the full coverage width of EO equipment in sector scanning mode is proposed. Second, the constraint of no interval missing and the model of the speed-to-high ratio constraint are established, and the constraints of other factors are addressed in the context of the problem situation. Third, a coverage efficiency index is proposed for the boustrophedon coverage of a rectangular area, and a comprehensive coverage index is established. Finally, task parameter planning algorithms are designed, based on Immune Algorithm (IA), Grey Wolf Optimization (GWO) and Variable Neighborhood Search (VNS), respectively. The simulation results showed that the designed algorithms, based on IA, GWO and VNS, can effectively solve task planning problems. In general, IA is more suitable for offline occasions, VNS is suitable for online real-time planning, and GWO has characteristics between the two. The coverage process, based on optimized parameters, meets all constraints, has higher search efficiency and does not miss areas, proving the correctness of these models and the effectiveness of the planning algorithm. The research presented in this paper provides a technical basis for efficient and fully automated target search and rescue.

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3.

Addressing the challenges of complex scheduling and trajectory coordination in dual-pod aerial refueling missions for heterogeneous unmanned aerial vehicle (UAV) formations, this paper proposes a hierarchical refueling joint scheduling and collision-aware trajectory planning method based on Multi-Agent Proximal Policy Optimization (MAPPO). A scheduling model is first developed that incorporates task constraints across fighter aircraft, wingmen, and UAVs. The MAPPO framework integrates refueling time windows, fuel states, and platform types into the state representation to output refueling sequences and refueling point assignments, enhanced by priority weighting and time penalty mechanisms. At the trajectory level, Dubins curves and trajectory homotopy are combined to enforce temporal synchronization and spatial deconfliction, ensuring safe and punctual rendezvous. Simulation results demonstrate that the proposed method outperforms baseline algorithms, including IQL, QMIX, and VDN. Specifically, the average estimated convergence episodes are reduced by over 65%, representing a substantial enhancement in the operational efficiency of dual-pod aerial refueling systems.