This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as $$\varepsilon$$ε -imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.

The objective of coverage path planning is to guarantee that unmanned aerial vehicles (UAVs) can achieve complete coverage of the target region. In previous research works, the operation mode of UAVs was to be individually responsible for the coverage tasks of each sub-region. Nevertheless, in the present study, multiple UAVs operate in a collaborative manner within the entire search area. This approach enables more efficient coverage and a more flexible accomplishment of the coverage mission. Given that the traditional methods for solving the UAV coverage path planning problem often result in a high overall planning cost, this paper puts forward multiple UAVs coverage path planning algorithm based on Q-learning. To reduce the time required for UAVs to complete the coverage search task, a grid based rotating region division algorithm is employed to minimize the area yet to be searched. By formulating the UAV coverage path planning model, the path planning problem is transformed into a multi-objective function optimization problem. The Double-Q-learning algorithm is utilized to balance the global search and local exploitation of the algorithm. A total cost function, which takes into account both the distance cost and the turning cost, is adopted to iteratively optimize the path planning. The results of simulation experiments demonstrate that the paths planned by the proposed algorithm can enable multiple UAVs to fully cover the target region in various target areas at a lower total cost.

Aiming at the attitude control problem of fixed wing UAV, this paper introduces S-plane control, which has good control effect in the field of underwater UAV, into the attitude control of UAV. At the same time, aiming at the problem that the coefficient setting of parameters in S-plane control completely depends on experience and cannot be adjusted adaptively, the radial basis function neural network (RBFNN) is introduced, and a neural network S-plane control model which can realize on-line adaptive adjustment of the coefficient of parameters in S-plane control is proposed. The simulation results based on the data of a certain UAV show that compared with the S-plane control, the proposed neural network S-plane control model has the characteristics of fast response speed, strong anti-interference ability, and strong robustness. In addition, it also has the function of adaptive adjustment, which shows good control performance.

This paper addresses the problem of high communication frequency in the multi-rotor unmanned aerial vehicles swarm system. A novel sliding mode control method based on event-triggering is proposed, using a dual-loop structure control model based on sliding mode control of multi-rotor UAVs swarm. The model consists of a position subsystem and an attitude subsystem, where the position subsystem serves as the outer loop control object, and the attitude subsystem serves as the inner loop auxiliary control object. In the position subsystem, a sliding mode controller with event triggers is used to achieve position control of the multi-rotor UAVs swarm by introducing sliding mode surfaces and sliding mode variables. The stability of the system under this control strategy is proved by the Lyapunov stability theorem, and the absence of the Zero phenomenon is also proved. Simulation results demonstrate that the proposed control scheme reduces the reliance on high-frequency sampling of multi-rotor UAVs communication. This research provides a useful reference for the application of multi-rotor UAVs swarms in collaborative path following.

The positioning results of terrain matching in flat terrain areas will significantly deteriorate due to the influence of terrain nonlinearity and multibeam measurement noise. To tackle this problem, this study presents the Pulse-Coupled Neural Network (PCNN), which has been effectively utilized for image denoising. The interconnection of surface terrain data nodes is achieved through PCNN ignition, which serves to alleviate the reduction in terrain similarity caused by measurement error. This enables the efficient selection of terrain data, ensuring that points with high measurement accuracy are preserved for terrain matching and positioning operations. The simulation results illustrate that the suggested methodology effectively removes terrain data points with low measurement accuracy, thereby improving the performance of terrain matching and positioning.