The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.

中文翻译：

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基于生成对抗网络的多自主水下航行器一致性协同狩猎方法

时变的洋流和水声通信的延迟导致单个自主水下航行器（AUV）跟踪目标的不确定性和多AUV协调的不一致，使得多个AUV难以形成狩猎联盟。针对上述问题，本文提出了基于生成对抗网络（GAN）的多AUV一致协同狩猎方法。首先，针对水下3D环境建立AUV的三维（3D）运动学模型。其次，结合拉普拉斯矩阵，建立理想环境下狩猎联盟的拓扑结构，计算AUV的控制率。最后，使用 GAN 网络模型，环境干扰后的控制关系作为生成模型的输入。理想环境下的控制率作为判别模型的比较对象。利用GAN的迭代训练生成适应当前干扰环境的控制率，结合多AUV拓扑搜索模型实现非合作目标的成功搜索，实验结果表明，该算法将平均搜索时间降低至62.53 s，狩猎成功率提高到84.69%，比粒子群优化-恒模算法（PSO-CMA）算法高1.17%。