复杂环境下异构无人机集群协同目标搜索方法

A Collaborative Target Search Method for Heterogeneous UAV Swarms in Complex Environments

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较强的低空无人机,提出了一种新颖的异构无人机集群协同目标搜索方法,根据不同无人机的特性快速实现区域搜索以及多目标的精准搜索。高空无人机采用基于数字信息素的区域搜索算法确定目标存在区域,在较短时间内实现区域覆盖。低空无人机在其指引下前往目标区域,并根据目标信号强度采用改进狼群算法实现对目标的精准搜索。对于传统狼群算法信息共享不足、步长固定和易陷入局部最优的问题,采用粒子群算法、自适应参数调整和差分进化方法进行改进,提高了算法的全局寻优能力和收敛速度。仿真实验证明了所提出的模型与方法在复杂环境下目标搜索任务中的有效性和优越性。

To achieve efficient and accurate multi-target search of a UAV swarm,and with consideration of the wide detection ranges of high-altitude UAVs and the strong maneuverability of low-altitude UAVs in the swarm,a novel cooperative target search method is proposed for heterogeneous UAV swarms,which can realize rapid regional search and accurate multi-target search according to the characteristics of different UAVs.First,the high-altitude UAVs use a region search algorithm based on digital pheromones to determine the target's presence area,which realizes area overlapping in short time.The low-altitude UAVs then proceeds to the target area guided by the high-altitude UAVs and use a precise search algorithm based on an Improved Wolf Pack Algorithm(IWPA)to accurately locate the target.To address the limitations of traditional wolf pack algorithm,such as insufficient information sharing,fixed step size,and susceptibility to local optima,improvements have been made by using particle swarm optimization,adaptive parameter adjustment,and differential evolution methods.These enhancements improve the algorithm's global optimization capability and convergence speed.Simulation experiments demonstrate the effectiveness and superiority of the proposed model and method for target search tasks in complex environment.