一种三维群无人机并行式多目标搜索协调控制策略

A 3D Parallel Multi-target Search Coordination Control Strategy for Swarm UAVS

针对未知环境下三维群无人机多目标搜索问题,本文提出了一种三维群无人机并行式多目标搜索协调控制策略.首先,在基于目标响应阈值多目标分配模型(TRT)上,引入了闭环调节和合作协同的策略,提出了一种改进响应阈值多目标任务分配模型(ITRT),有效解决了群无人机子群分布不均匀问题,提高了群无人机并行式搜索的效率.其次,将二维扩展式微粒群(EPSO)的群机器人协调控制算法引入到三维群无人机上,并将一种改进自适应惯性权重的方法与三维扩展式粒子群相结合,提出了一种改进自适应惯性权重的三维扩展式微粒群算法(IAEPSO),有效解决了微粒易陷入局部最优点和搜索效率过低的问题.最后,将两种改进的方法(ITRT+IAEPSO)相结合实现群无人机并行式搜索.相比于传统并行式多目标搜索的扩展式粒子群算法(TRT+EPSO),本方法的系统能耗和搜索耗时大大减少,数值仿真验证了该方法的有效性.

A parallel coordinated search control strategy of 3 D swarm unmanned aerial vehicle( UAV) is proposed to solve the multi-target search problem in an unknown environment. First,two strategies-closed-up regulation and cooperation are introduced with the use of the self-organizing task division of the target response thresholdbased swarm robots. An improved target response threshold self-organizing task division mode of seed group size homogenization is proposed to handle the problem of uneven distribution of UAVS subgroups,such that the efficiency of parallel search of UAVS can be implicitly improved. Second,the coordinated control algorithm of swarm robot based on 2 D extended particle swarm optimization is applied to 3 D swarm UAV,whereby an improved adaptive inertia weight is developed,and a 3 D extended particle swarm optimization with an improved adaptive inertia weight( IAEPSO) is proposed to alleviate the local optimum problem and low searching efficiency. Finally,two improved methods( ITRT + IAEPSO) are used in the parallel search of swarm UAVS. Compared with the traditional parallel multi-target search extended particle swarm optimization( TRT+ EPSO),the proposed methods can reduce the search time and energy consumption of system and the numerical simulations are performed to verify the effectiveness of the proposed methods.