随着科学技术的进步以及作战理论的发展，无人机集群逐渐走进现代战场，成为决定战争胜负的核心力量。为了充分发挥无人机集群的优势，需要设计科学、高效的任务协同技术，为其提供行之有效的任务规划和决策方法。本文以无人机集群协同作战为问题背景，研究了战场环境下协同目标搜索、协同任务分配以及协同航迹规划三项关键技术，主要内容如下：

建立考虑机动性能约束的集群运动模型及集群通信拓扑结构。采用数字高程模型建立初始地形环境，并通过分析不同威胁源的特点建立相应的数学模型。在此基础上，将初始地形环境与威胁等效模型进行融合，得到等效数字地图，为后续的研究奠定基础。

为实现无人机集群高效且精准的多目标搜索，考虑集群成员中探测范围广的高空无人机和机动能力强的低空无人机，提出一种异构无人机集群协同目标搜索方法。针对高空无人机区域搜索任务，采用基于数字信息素的区域搜索算法，以信息素浓度和区域覆盖率为目标，通过设计信息素的更新规则，避免了部分区域的重复搜索。针对低空无人机精准搜索任务，采用基于改进狼群算法的精准搜索算法，通过自适应参数调整和差分进化方法改进传统狼群算法步长固定和易陷入局部最优的问题，进一步提高搜索精度和搜索效率。仿真结果表明所提出的方法能够有效完成搜索任务，改善整体搜索性能。

为解决无人机集群协同打击任务分配问题，考虑集群航迹代价、武器成本等关键指标，建立无人机集群协同打击任务分配优化模型，并采用切面地形跟随法估算战场环境的三维航程。针对传统单目标优化方法易受主观偏好影响的问题，提出一种多目标离散狼群算法。该算法采用Pareto非支配排序构建精英集，在生成初始种群时融入反向学习策略以改善初始种群质量，在离散空间下对狼群智能行为机制重新设计以提升算法的寻优能力。仿真结果表明所提出的方法在保持Pareto最优解多样性的同时具有较好的收敛性能，能够为任务提供多样化分配方案。

为满足战场环境中集群协同打击目标的要求，实现打击效能最大化，考虑无人机自身性能约束及多机时空协同关系，建立无人机集群协同打击航迹规划优化模型。引入共同进化思想，提出一种多目标狼群共同进化算法。每架无人机独立地采用多目标狼群算法进行航迹规划，通过协同性系数排序选择代表个体进行信息交互，从而实现集群时空协同。在生成航迹点之后，采用B样条插值算法进行航迹平滑，使得最终生成的航迹满足实际飞行要求。仿真结果表明所提出的方法能够有效满足无人机集群协同航迹规划的约束，得到多组满足不同性能要求的结果。

在上述研究的基础上，设计并开发集成目标搜索、对地打击两种任务的无人机集群协同作战仿真系统，实现具备参数调整和仿真过程可视化功能的人机交互界面，为决策者提供一定参考支持，以合理配置战争资源，实现更高的作战效能。

With the advancement of science and technology and the upgrading of combat theory, UAV swarms have gradually entered modern battlefields and become the core force determining the outcome of wars. In order to fully leverage the advantages of UAV swarms, it is necessary to design scientific and efficient task collaboration technologies to provide an effective set of task planning and decision-making methods for UAV swarm systems. This paper studies three key technologies in collaborative target search, collaborative task allocation, and collaborative trajectory planning for UAV swarm operations in complex battlefield environments. The main research contents are as follows:

A swarm motion model considering maneuverability constraints and swarm communication topology structure is established. A digital elevation model is used to create the initial terrain environment, and corresponding mathematical models are established by analyzing the characteristics of different threat sources. On this basis, the initial terrain environment and the threat equivalent model are fused to obtain an equivalent digital map, laying the foundation for subsequent research.

To efficiently and accurately achieve multi-target search with a UAV swarm, this paper proposes a novel cooperative target search method for heterogeneous UAV swarm. This method takes into consideration high-altitude UAVs with wide detection ranges and low-altitude UAVs with strong maneuverability within the swarm. For the regional search tasks of high-altitude UAVs, a regional search algorithm based on digital pheromones is proposed. This algorithm uses pheromone concentration and region coverage as objectives and designs pheromone update rules to avoid repeated searches in certain areas. For accurate search tasks of low-altitude UAVs, an improved precise search algorithm based on the wolf pack algorithm is proposed. By using adaptive parameter adjustment and differential evolution methods, issues with the traditional wolf pack algorithm, such as fixed step size and susceptibility to local optima, are addressed, further enhancing search accuracy and efficiency. Simulation results demonstrate that the proposed model and method can effectively complete the search task and improve the overall search performance.

In response to the problem of task allocation for UAV swarm collaborative strikes, key indicators such as swarm trajectory cost and weapon cost are taken into consideration. An optimization model for cooperative strike task allocation in UAV swarms is established, with the facet terrain following method used for estimating the three-dimensional flight path in the battlefield environment. Traditional single-objective optimization methods are susceptible to limitations of subjective preferences. To address this, a multi-objective discrete wolf pack algorithm is proposed. This algorithm utilizes Pareto non-dominance sorting to construct an elite set, incorporates a reverse learning strategy to enhance the quality of the initial population, and redesigns the intelligent behavior mechanism of the wolf pack in discrete space to boost the algorithm's optimization capabilities. Simulation results demonstrate that the proposed method maintains the diversity of Pareto optimal solutions while exhibiting good convergence performance, thereby providing diversified allocation schemes for tasks.

In order to meet the requirements of cooperative target strikes in battlefield environments and maximize strike efficiency, considering the performance constraints of the UAVs themselves and the spatio-temporal coordination relationships among multiple UAVs, an optimization model for cooperative strike trajectory planning of UAV swarms is established. The concept of co-evolution is introduced, and a multi-objective wolf pack co-evolution algorithm is proposed. Each UAV independently uses the multi-objective wolf pack algorithm for trajectory planning, and selects representative individuals for information interaction through cooperative coefficient sorting, thereby achieving spatio-temporal coordination of the swarm. After generating the trajectory points, the B-spline interpolation algorithm is used for trajectory smoothing, so that the final generated trajectory meets the actual flight requirements. Simulation results show that the proposed method can effectively meet the constraints of cooperative trajectory planning of UAV swarms, and obtain multiple sets of results that meet different performance requirements.

Based on the aforementioned research, a UAV swarm cooperative combat simulation system that integrates target search and ground strike missions is designed and developed. This system features a human-machine interface with capabilities for parameter adjustment and simulation process visualization, providing decision-makers with reference support to allocate war resources rationally and achieve higher combat efficiency.

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