基于改进DPSO的故障下多无人机协同任务规划  

Multi-UAV cooperative mission planning under faults based on IDPSO

作　　者：邵士凯 李厚振 赵渊洁 SHAO Shikai;LI Houzhen;ZHAO Yuanjie(School of Electrical Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China)

机构地区：[1]河北科技大学电气工程学院,石家庄050018

出　　处：《兵器装备工程学报》2023年第6期213-222,共10页Journal of Ordnance Equipment Engineering

基　　金：国家自然科学基金项目(61903122);河北省自然科学基金项目(F2021208015);河北省教育厅科学技术研究项目(BJ2021003)。

摘　　要：针对故障后多无人机协同任务规划问题,提出了一种基于改进离散粒子群算法并结合匈牙利算法的任务重分配方法。首先,采用匈牙利算法进行故障下无人机队形的快速重新排列;然后,引入柯西算子提升离散粒子群算法的全局搜索能力,以增强搜索空间,同时,还提出了非线性时变的变异策略,加快算法的收敛速度,综合改进的离散粒子群算法不仅加快了收敛速度,并且解的最优性也得到了提高,此外,在分配过程中,考虑了环境障碍信息,分配结果更贴近实际也更加合理;最后,运用基本粒子群算法进行无人机的航迹规划,并在三维空间中进行了仿真实验,结果表明:所设计的算法能够有效提升任务分配的寻优结果,为多无人机出现故障后协同任务分配问题提供了理论依据。Aiming at the multi-UAV cooperative mission planning after faults,this paper proposes a task reassignment method based on a combination of improved discrete particle swarm optimization(IDPSO)and Hungarian algorithm.Firstly,the Hungarian algorithm is used to rapidly rearrange the formation of UAVs under faults.Then,Cauchy operator is introduced to improve the global search ability of IDPSO to enhance the search space.At the same time,a nonlinear time-varying mutation strategy is also proposed to accelerate the convergence speed of the algorithm.The IDPSO not only accelerates the convergence speed,but also improves the optimality of the solution.In addition,in the process of allocation,the environmental obstacles are taken into consideration,so the allocation results are more realistic and reasonable.Finally,the basic particle swarm optimization algorithm is used for UAV track planning,and the simulation experiments are carried out in three-dimensional space.The results show that the designed algorithm can effectively improve the optimization results of the task allocation,which provides a theoretical basis for cooperative task allocation after multiple-UAV faults.

关 键 词：无人机故障 任务分配 多机协同 改进离散粒子群算法 柯西算子 非线性时变变异策略 匈牙利算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]