分布式多AUV协同搜索方法

doi:10.12305/j.issn.1001-506X.2022.05.29

Abstract

Abstract:

Aiming at the problems of communication delay and easy failure of a single autonomous underwater vehicle (AUV) in underwater cooperative search, a new method using distributed cooperative structure and rolling optimization strategy is proposed, which optimizes the objective function based on target existence probability, environmental uncertainty and coordinative pheromone by using the improved brain storm optimization(BSO) algorithm. Simulation results show that the proposed method can avoid collision and still be able to search all targets in the case of communication delay. Through simulation, the recommended value range of key parameters of the proposed method is given, and it is verified that the failure of individual AUV in the search process has little impact on the overall search effect, and the method has strong real-time and robustness.

Key words: autonomous underwater vehicle (AUV), cooperative search, coordination pheromone, improved brain storm optimization (BSO) algorithm

CLC Number:

TP249

Yongqi GAO, Weiqiang MA, Linsen ZHANG, Peng WANG, Miao ZHAO. Distributed multi-AUVs cooperative search method[J]. Systems Engineering and Electronics, 2022, 44(5): 1670-1676.

Figures/Tables 11

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参数符合	参数意义	值
p_d	传感器探测概率	0.96
p_f	传感器虚警概率	0.04
p₀	目标存在阈值	0.75
g_n	高斯分布峰值	0.324 8
σ²	高斯分布方差	0.49
τ	不确定度递减因子	0.1
M	充分大正数	1 000
H	较大协调信息素	5
L	较小协调信息素	1

算法	寻得目标数量/个	每步决策时间/s
GDBSO	8.80	0.704 0
BSO	8.24	0.924 9

子目标函数影响系数	寻得目标数量/个
k₁=0.2、k₂=0.6、k₃=0.2	8.38
k₁=0.3、k₂=0.5、k₃=0.2	8.60
k₁=0.4、k₂=0.4、k₃=0.2	8.80
k₁=0.5、k₂=0.3、k₃=0.2	8.58
k₁=0.6、k₂=0.2、k₃=0.2	8.46

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