The challenge of search inefficiency arises when multiple UAV swarms conduct dynamic target area searches in unknown environments. The primary sources of this inefficiency are repeated searches in the target region and the dynamic motion of targets. To address this issue, we present the distributed adaptive real-time planning search (DAPSO) technique, which enhances the search efficiency for dynamic targets in uncertain mission situations. To minimize repeated searches, UAVs utilize localized communication for information exchange and dynamically update their situational awareness regarding the mission environment, facilitating collaborative exploration. To mitigate the effects of target mobility, we develop a dynamic mission planning method based on local particle swarm optimization, enabling UAVs to adjust their search trajectories in response to real-time environmental inputs. Finally, we propose a distance-based inter-vehicle collision avoidance strategy to ensure safety during multi-UAV cooperative searches. The experimental findings demonstrate that the proposed DAPSO method significantly outperforms other search strategies regarding the coverage and target detection rates.

To address the limitations of the RRT-series algorithm and its variants, which have demonstrated a lack of dynamic adaptability in various environments, poor path quality, slow convergence rates, and a tendency to become trapped in local optima, this paper aims to propose a dynamic multi-strategy path-planning algorithm based on EG-DRRT* (Evolutionary Game-Theoretic Dynamic RRT*). The integration of evolutionary game theory allows the algorithm to use the concept of dynamically updating replicators to develop the payoff function for a fusion of multi-strategies. This enables a dynamic adjustment of the usage ratios of RRT*, Dijkstra, and Goal Bias algorithms. This approach directs the search process toward converging on the goal point, ultimately achieving an ESS (Evolutionarily Stable Strategy) equilibrium. The experimental results reveal that EG-DRRT* successfully establishes a dynamic balance between global exploration and local optimization across various environments, demonstrating remarkable adaptability and robustness. Additionally, EG-DRRT* shows substantial advantages compared to existing algorithms.

Accurate and rapid aerodynamic prediction is essential for projectile trajectory simulation and digital twin driven exterior ballistic systems, where conventional CFD methods are computationally expensive and difficult to deploy in real-time applications. To address this challenge, this paper proposes a primary-residual dual-stage surrogate modeling method based on a multilayer perceptron (MLP). A primary prediction model for aerodynamic parameters is constructed using CFD simulation data, where Mach number and angle of attack are used as input variables to establish the global nonlinear aerodynamic mapping. By introducing a residual learning mechanism, a compensation model is established to address fitting deviations in highly nonlinear regions, thereby correcting the nonlinear biases of the primary model predictions. The residual network further captures localized nonlinear discrepancies not fully resolved by the primary model, significantly improving prediction accuracy in high-Mach-number and large-angle-of-attack regions. Comparative validation against polynomial fitting, RBF, and single-stage MLP models shows that the proposed method achieves the best overall performance. For example, the RMSE is reduced from 2.91 to 0.87 for lift prediction and from 0.634 to 0.086 for pitching moment prediction. Overall, the proposed model reduces the average RMSE by approximately 50% across all aerodynamic parameters, demonstrating stronger robustness, improved generalization capability, and practical engineering applicability for digital twin based projectile exterior ballistic prediction.

To address the strong nonlinearity of aerodynamic loads during projectile exterior ballistic flight and the difficulty in accurately modeling fuze dynamic responses, this paper proposes a data-driven modeling and simulation method for fuze exterior ballistic dynamics. A high-fidelity aerodynamic database covering a range of Mach numbers and angles of attack is constructed based on CFD (Computational Fluid Dynamics) simulations. An MLP (Multilayer Perceptron) neural network is then employed to develop an aerodynamic surrogate model, enabling continuous representation of aerodynamic loads within the given sample space. The results show that, within the data coverage range, the proposed model is able to capture the nonlinear variation in aerodynamic parameters and shows improved prediction accuracy compared with the polynomial fitting method. Specifically, for typical aerodynamic parameters, the RMSE (Root Mean Square Error) is reduced from 5.758 to 0.223, the MAE (Mean Absolute Error) is reduced to 0.099, and the R² (Coefficient of Determination) approaches 1. On this basis, the aerodynamic surrogate model is embedded into a six-degree-of-freedom projectile–fuze exterior ballistic dynamics model via the secondary development interface of ADAMS 2020 (Automated Dynamic Analysis of Mechanical Systems), enabling coupled simulation between aerodynamic loads and multibody dynamics. Comparison with firing table data indicates that, under typical operating conditions, the relative deviation of ballistic parameters is generally better than 94%, demonstrating that the proposed method can reasonably reproduce the projectile exterior ballistic characteristics. Furthermore, based on the coupled dynamics model, the dynamic response characteristics of the fuze moving body during the exterior ballistic phase are analyzed. The results indicate that the axial forward overload of the moving body increases significantly with the initial nutation angle, and the variation in the axial projection of gravity induced by nutation plays an important role in its transient response. The proposed approach provides a useful reference for the dynamic response analysis and safety evaluation of fuzes.

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes.

To address the challenges of inefficient convergence in UAV swarms under complex environments due to static position allocation (SPA), as well as the tendency of traditional artificial potential field (APF) obstacle avoidance to get stuck in local optima, this paper proposes a formation control method (DPAF-SA) based on dynamic position allocation (DPA) and APF-SA fusion, grounded in the principle of consensus and the simulated annealing (SA) algorithm. First, the formation position allocation is formulated as an online combinatorial optimization problem. Based on this framework, a dynamic position allocation and dynamic virtual center mechanism is designed to solve the optimal “UAV-position point” mapping in real time, minimizing the total convergence cost of the swarm. Second, to address the local optimum trap and decoupling issues in APF, the global search capability and probabilistic jump mechanism of SA are integrated into APF. This enables optimization of the consistency control input, ensuring tight coupling between efficient obstacle avoidance and formation maintenance. Finally, a high-fidelity HIL simulation platform based on Unity3D 2022.3.2. was established to validate the engineering feasibility and real-time robustness of the proposed algorithm. Simulation results demonstrate that, compared with the representative baseline model, the proposed method achieves improvements of approximately 46.1%, 24.5%, and 39.6% in formation accuracy, convergence performance, and safety margin, respectively, validating its effectiveness.