Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. However, current research on adversarial patches, especially those targeting UAV object detection, is limited. This scarcity is notable given the complex and dynamically changing environment inherent in UAV image acquisition, which necessitates the development of more robust adversarial patches to achieve effective attacks. To address the challenge of adversarial attacks in UAV high-altitude reconnaissance, this paper presents a robust adversarial patch generation framework. Firstly, the dataset is reconstructed by considering various environmental factors that UAVs may encounter during image collection, and the influences of reflections and shadows during photography are integrated into patch training. Additionally, a nested optimization method is employed to enhance the continuity of attacks across different altitudes. Experimental results demonstrate that the adversarial patches generated by the proposed method exhibit greater robustness in complex environments and have better transferability among similar models.

To tackle the challenges presented by the two‐player zero sum game (TZSG) in three‐dimensional space, this study introduces an enhanced deep Q‐learning (DQN) algorithm that utilizes long short term memory (LSTM) network. The primary objective of this algorithm is to enhance the temporal correlation of the TZSG in three‐dimensional space. Additionally, it incorporates the hindsight experience replay (HER) mechanism to improve the learning efficiency of the network and mitigate the issue of the “sparse reward” that arises from prolonged training of intelligence in solving the TZSG in the three‐dimensional. Furthermore, this method enhances the convergence and stability of the overall solution.An intelligent training environment centred around an airborne agent and its mutual pursuit interaction scenario was designed to proposed approach's effectiveness. The algorithm training and comparison results show that the LSTM‐DQN‐HER algorithm outperforms similar algorithm in solving the TZSG in three‐dimensional space. In conclusion, this paper presents an improved DQN algorithm based on LSTM and incorporates the HER mechanism to address the challenges posed by the TZSG in three‐dimensional space. The proposed algorithm enhances the solution's temporal correlation, learning efficiency, convergence, and stability. The simulation results confirm its superior performance in solving the TZSG in three‐dimensional space.

Aiming at the problem of random environment interference in the process of strategy interaction and the behavioral evolution of an aerial attack–defense game, this paper considers the influence of the difference in the performance and value between both game players in terms of strategy evolution; explores the randomness of the complex battlefield environment, the uncertainty of the behavioral state of game players, and the limitations of the emergent situation; constructs a mathematical model of the stochastic evolution of an aerial-coordinated attack–defense game in uncertain environments; and studies the stability of the strategy interaction and behavioral decision-making process of both players of the aerial attack–defense game. Simulation results show that many factors of the performance and value between both game players have a greater impact on the strategy evolution trend in both game players, which not only causes changes in the results of the strategy selection but also affects the rate of strategy evolution for the game players. In addition, random environmental factors cause a certain degree of interference to the strategy evolution process of the game players, which usually accelerates the game players’ strategy evolution rate and greatly affects the evolution process of the game players’ strategy. This study can provide a theoretical basis and feasible reference for improving mission decision-making, response mechanisms, and system modeling of an aerial attack–defense game, which has important theoretical value and practical significance.

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research.

Driven by the trend toward miniaturization and wearability in electronic devices, traditional rigid printed circuit boards (PCBs) are increasingly inadequate to meet emerging demands. Additive manufacturing technologies, exemplified by direct writing and 3D printing, offer novel solutions for flexible electronics fabrication by presenting advantages such as rapid prototyping and free design freedom. This paper reviews the evolution from rigid to flexible electronics, with a particular focus on the merits of materials such as liquid metal inks. It examines the current research status of printed electronics and 3D-printed PCBs, analyzes key technical challenges, and discusses prospects for future industrialization. Practical engineering applications of the technology are also demonstrated.