多基地多目标无人机协同任务规划算法研究

doi:10.3969/j.issn.1009-086x.2021.02.008

现代防御技术 ›› 2021, Vol. 49 ›› Issue (2): 49-56.DOI: 10.3969/j.issn.1009-086x.2021.02.008

多基地多目标无人机协同任务规划算法研究

潘楠^1a, 刘海石^1a, 陈启用^1b, 颜礼贤^1a, 郭晓珏²

1.昆明理工大学a.民航与航空学院;b.材料科学与工程学院,云南昆明 650500;
2.昆明智渊测控科技有限公司,云南昆明 650500

收稿日期:2020-10-14 修回日期:2020-12-07 出版日期:2021-04-20 发布日期:2021-04-30
作者简介:潘楠(1986-),男,安徽怀远人。副教授,博士,主要从事人工智能和智能量测方面研究。通信地址:650500 云南昆明昆明理工大学公教楼343 E-mail:nanpan@kust.edu.cn

Study on Cooperative Mission Planning Algorithm for Multi-Base and Multi-Target UAV

PAN Nan^1a, LIU Hai-shi^1a, CHEN Qi-yong^1b, YAN Li-xian^1a, GUO Xiao-jue²

1. Kunming University of Science & Technology,a. Faculty of Civil Aviation and Aeronautical;b. Faculty of Materials Science and Engineering,Yunnan Kunming 650500,China;
2. Kunming Zhiyuan Measurement and Control Technology Co.,Ltd.,Yunnan Kunming 650500,China

Received:2020-10-14 Revised:2020-12-07 Online:2021-04-20 Published:2021-04-30

摘要/Abstract

摘要： 在多基地多目标多无人飞行器(unmanned aerial vehicle,UAV)的协同任务规划这类约束条件众多、复杂且耦合的多目标优化与决策问题中,利用传统的粒子群优化算法在寻优时容易陷入局部最优,为此,提出了一种基于模拟退火的混合粒子群算法。基于攻打任务背景,综合考虑无人机的物理性能约束,搭建航迹长度最小适应度函数和威胁代价最小适应度函数以构造目标函数,先利用Voronoi图以及Dijkstra算法进行航迹规划,再利用基于模拟退火的混合粒子群算法进行任务分配。仿真结果表明:所提算法融合了模拟退火算法、粒子群优化算法的优点,能快速求解UAV任务规划的近似最优解,且与粒子群优化算法和模拟退火算法相比,在进化次数足够多的情况下该方法得到的结果更优。

关键词: 无人飞行器, 攻打, 任务规划, 迪杰斯特拉算法, 模拟退火算法, 粒子群算法

Abstract: In the multi-objective optimization and decision-making problem of multi-objective and multi-unmanned aerial vehicle (UAV),which has many constraints,complex and coupling,the traditional particle swarm optimization (PSO) algorithm is easy to fall into the local optimal in the optimization.Therefore,a hybrid particle swarm optimization algorithm based on simulated annealing (SA-PSO) is proposed.Based on the background of the attack task,comprehensively considers the physical performance constraints of the UAV,builds the minimum fitness function of the track length and the minimum fitness function of the threat cost to construct the objective function.First,use the Voronoi diagram and the Dijkstra algorithm for path planning,then a hybrid particle swarm optimization algorithm based on simulated annealing is used for task assignment.The simulation results show that the proposed algorithm combines the advantages of simulated annealing algorithm (SA) and particle PSO,which can quickly solve the approximate optimal solution of UAV task planning and is compatible with PSO algorithm.Compared with the SA algorithm,better results is obtained when there are enough evolution times.

Key words: unmanned aerial vehicle (UAV), attack, mission planning, Dijkstra algorithm, simulated annealing (SA) algorithm, particle swarm optimization (PSO) algorithm

中图分类号:

潘楠, 刘海石, 陈启用, 颜礼贤, 郭晓珏. 多基地多目标无人机协同任务规划算法研究[J]. 现代防御技术, 2021, 49(2): 49-56.

PAN Nan, LIU Hai-shi, CHEN Qi-yong, YAN Li-xian, GUO Xiao-jue. Study on Cooperative Mission Planning Algorithm for Multi-Base and Multi-Target UAV[J]. Modern Defense Technology, 2021, 49(2): 49-56.

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多基地多目标无人机协同任务规划算法研究

Study on Cooperative Mission Planning Algorithm for Multi-Base and Multi-Target UAV

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