基于改进蝗虫优化算法考虑任务威胁的多无人机协同航迹规划

doi:10.12382/bgxb.2023.0937

摘要/Abstract

摘要：

为使多无人机(UAV)在面临不同程度的任务威胁环境时能够高效的执行任务,研究并设计一种新型协同航迹规划算法,以综合代价为目标函数,利用改进的蝗虫优化算法对构建的航迹规划模型进行求解。分析传统蝗虫算法的原理以及不足,提出改进策略,即引入基于逻辑斯蒂函数的非线性递减策略;针对改进之后的算法进行仿真测试,并与其他算法进行对比,验证算法的应用效果。仿真结果显示,相对于其他算法,改进算法具有明显的优势,收敛速度更高,航迹代价更低,可为UAV作战效能提升提供支撑。

关键词: 多无人机协同, 航迹规划, 改进蝗虫算法, 任务威胁

Abstract:

To enable multiple unmanned aerial vehicles (UAVs)to efficiently execute tasks when facing varying degrees of mission threat environments, a collaborative route planning algorithm of UAVs based on improved grasshopper optimization algorithm is proposed. A route planning model is established by taking the comprehensive cost as an objective function. The grasshopper optimization algorithm is improved by introducing a nonlinear descent strategy based on the logistic function. The feasibility of the improved grasshopper optimization algorithm is verified through simulation experiment. The experimental results showed that the improved grasshopper optimization algorithm has faster convergence speed and global search ability, which can provide support for improving the combat effectiveness of unmanned aerial vehicles.

Key words: multi-UAV collaboration, route planning, improved grasshopper optimization algorithm, task threat

中图分类号:

V249

郭志明, 娄文忠, 李涛, 张梦宇, 白子龙, 乔虎. 基于改进蝗虫优化算法考虑任务威胁的多无人机协同航迹规划[J]. 兵工学报, 2023, 44(S2): 52-60.

GUO Zhiming, LOU Wenzhong, LI Tao, ZHANG Mengyu, BAI Zilong, QIAO Hu. Collaborative Route Planning of Multiple Unmanned Aerial Vehicles Considering Task Threats Based on Improved Grasshopper Optimization Algorithm[J]. Acta Armamentarii, 2023, 44(S2): 52-60.

图/表 12

图1 多UAV协同航迹规划场景

Fig.1 Multi-UAV collaborative trajectory planning scenario

图2 UAV航迹模型

Fig.2 UAV trajectory model

图3 地面雷达探测模型

Fig.3 Ground radar detection model

图4 防空导弹拦截区域模型

Fig.4 Air defence missile interception area model

图5 雷达威胁

Fig.5 Radar threats

图6 线性缩减因子c的变化

Fig.6 Variation of linear shrinkage factor c

图7 改进GOA流程图

Fig.7 Flowchart of the improved grasshopper optimisation algorithm

表1 地面雷达信息

Table 1 Ground-based radar information

雷达威胁坐标/km	威胁半径/km
(20,60)	5
(50,30)	3
(30,70)	5
(80,80)	10
(20,80)	5
(80,30)	5
(60,70)	5
(80,50)	6

表2 防空导弹信息

Table 2 Anti-aircraft missile information

防空导弹威胁坐标/km	威胁半径/km
(10,60)	8
(40,50)	6
(60,50)	5
(100,30)	8

图8 改进GOA航迹规划立体图和俯视图

Fig.8 Stereo and top views of the improved grasshopperopti misation algorithm for trajectory planning

表3 4种算法的航迹代价对比数据

Table 3 Comparative data of trajectory costs for 4 algorithms

算法	航迹代价
算法	平均航迹/km	最佳航迹/km	最差航迹/km
GA	75.7307	73.2124	78.2795
PSO	73.8909	72.3221	76.2391
GOA	73.6189	73.7284	75.2420
改进GOA	73.0829	72.1689	75.6542

图9 4种算法迭代过程中平均航迹综合代价的变化

Fig.9 Variation of average trajectory integration cost during the iteration of 4 algorithms

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