双属性概率图优化的无人机集群协同目标搜索

doi:10.3969/j.issn.1001-506X.2020.01.16

Abstract

Abstract:

To improve the target capture capability of unmanned aerial vehicle (UAV) in uncertain environments and the efficiency of multi-UAVs collaborative searching, the proposed method named improved co-evolutionary genetic algorithm (ICEGA) based on dual attribute probability maps is proposed for multi-UAVs cooperative target capturing. Firstly, according to the prior information of the environment, the flag bit is introduced to the original probability map, and the proposed environment probability model is built. Secondly, the flight rules of UAVs and the environment probability model are used together to establish the UAV motion model and the objective function. Finally, the distributed model between the UAVs is established, and the ICEGA algorithm is used to optimize the input heading angles set with cooperative decision-making. The experimental results show that the dual-attribute probability map working with the ICEGA algorithm can guarantee the optimal searching path, so that the UAV can accurately capture the target. At the same time, the comparable experiments prove that the ICEGA algorithm can improve the coordination between UAVs, make the flying path feasible and improve the efficiency of target capturing.

Key words: multi-unmanned aerial vehicles (UAVs), collaborative search, improved co-evolutionary genetic algorithm (ICEGA), dual attribute map, rolling optimization

CLC Number:

V279
TP2

Jie HUANG, Wei SUN, Yu GAO. Cooperative searching for the multi-UAVs based on dual-attribute probability model optimization[J]. Systems Engineering and Electronics, 2020, 42(1): 118-127.

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Γ₁	Γ₂	ω₁	ω₂	目标数目
0.45	0.2	0.15	0.2	7
0.35	0.2	0.2	0.25	5
0.30	0.15	0.25	0.3	6

UAV	网格位置(m, n)	航向角/(°)	编码
UAV₁	(1, 1)	45	1
UAV₂	(1, 3)	0	2

目标	位置(x, y)/m	网格位置(m, n)	属性p/(p, f)
T₁	(1 500, 800)	(15, 8)	0.8/(0.8, 1)
T₂	(1 200, 1 400)	(12, 14)	0.8/(0.8, 1)
T₃	(900, 800)	(9, 8)	0.8/(0.8, 1)

UAV	网格位置(m, n)	初始航向角ϕ/(°)	编码
UAV₁	(1, 1)	90	0
UAV₂	(1, 3)	0	2
UAV₃	(1, 11)	45	1

目标	位置(x, y)/m	网格位置(m, n)	属性(p, f)
T₁	(1 500, 800)	(15, 8)	(0.8, 1)
T₂	(1 200, 1 400)	(12, 14)	(0.8, 1)
T₃	(900, 800)	(9, 8)	(0.8, 1)
T₄	(600, 1600)	(6, 16)	(0.8, 1)
T₅	(300, 300)	(3, 3)	(0.8, 1)

Cooperative searching for the multi-UAVs based on dual-attribute probability model optimization

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