Collaborative Route Planning of Multiple Unmanned Aerial Vehicles Considering Task Threats Based on Improved Grasshopper Optimization Algorithm

doi:10.12382/bgxb.2023.0937

Abstract

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

CLC Number:

V249

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.

Figures/Tables 12

Fig.1 Multi-UAV collaborative trajectory planning scenario

Fig.2 UAV trajectory model

Fig.3 Ground radar detection model

Fig.4 Air defence missile interception area model

Fig.5 Radar threats

Fig.6 Variation of linear shrinkage factor c

Fig.7 Flowchart of the improved grasshopper optimisation algorithm

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

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

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

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

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

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