International Journal of Aerospace Engineering

To tackle the challenge of obstacle avoidance path planning for multiple unmanned aerial vehicles (UAVs) in intricate environments, this study introduces a Voronoi graph–based model to represent the obstacle-laden environment and employs a Markov decision process (MDP) for single UAV path planning. The traditional Q-learning algorithm is enhanced by adjusting the initial state of the Q-table and fine-tuning the reward and penalty values, enabling the acquisition of efficient obstacle avoidance paths for individual UAVs in complex settings. Leveraging the improved Q-learning algorithm for single UAVs, the Q-table is iteratively refined for a fleet of UAVs, with dynamic modifications based on the waypoints chosen by each UAV. This approach ensures the generation of collision-free paths for multiple UAVs, as validated by simulation results that showcase the algorithm’s effectiveness in learning from past training data. The proposed method offers a robust framework for practical UAV trajectory generation in complex environments.

This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan.

Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems.

This paper presents research on the use of anisogrid lattice structures in fighter wing applications. While the anisogrid lattice structure has been widely used in spacecraft structures, its implementation in main aircraft structures is still limited. The study is aimed at investigating the feasibility of utilizing an anisogrid lattice structure in fighter wing design. The analysis and optimization focus on determining the optimal weight of the composite wing structure, considering static, buckling, and flutter failure constraints. Various lift distributions, including triangular, Schrenk, and constant, are applied to evaluate the structure’s response to static failure caused by aerodynamic loads. The anisogrid structure design incorporates inclined lattice elements between ribs and spars, with spar arrangement in the wing box featuring an anisogrid configuration. The anisogrid lattice structure is expected to produce higher bending and torsional stiffness compared to conventional orthogonal structures, producing better flutter and buckling characteristics. The optimized wing structure successfully meets static, buckling, and flutter load requirements at speeds below 500 m/s. The study showcases triangular, Schrenk, and constant load distributions resulting in half-wing masses of 504, 571, and 707 kg, respectively. The results show that flutter and buckling loads are no longer the critical loads in wing structural design but static load.

To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sparsity with adaptive variable step size. In the proposed algorithm (CoSaMP with variable step size abbreviated as Vss-CoSaMP), the idea of approximating sparsity with adaptive step size was borrowed from the sparsity adaptive matching pursuit (SAMP) algorithm to determine the sparsity for the CoSaMP algorithm. The applicability of the CoSaMP algorithm was therefore expanded considerably. On this basis, a step size reduction was added as the iteration termination condition of an orthogonal frequency division multiplexing (OFDM) system. An adaptive variable step size algorithm was then put forward to address the CoSaMP algorithm’s sensitivity to step size. It could realize the required precision at different initial step sizes. A simulation was carried out to analyze the influence of pilot number and step size in an OFDM system on the algorithm. The algorithms, including SAMP, CoSaMP, and Vss-CoSaMP, were compared with two sparse channels, revealing that the Vss-CoSaMP algorithm overcame the problem of the CoSaMP algorithm, that is, the impossibility to forecast the channel sparsity. With the adaptive step size, the proposed algorithm could reach and achieve better accuracy than the CoSaMP algorithm. Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER).