Intelligent Control Method for AUV Formation under Different Communication and Positioning Methods: A Review
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摘要: 综述了国内外自主水下航行器(AUV)在光学和声学2种不同通信定位方式下的编队智能控制方法。首先, 归纳了AUV编队的基础模型, 这些模型是考虑通信定位约束时智能控制方法设计的基础。其次, 针对光学与声学2种通信定位方式, 归纳了考虑通信定位约束时的技术难点和智能控制方法, 并对研究成果进行了总结。最后, 对AUV编队智能控制的发展趋势进行了分析和探讨。可为AUV编队智能控制方法的设计提供参考, 对AUV编队的理论研究和工程化应用具有借鉴意义。Abstract: The formation of intelligent control methods for autonomous undersea vehicles(AUVs) under two different communication and positioning methods of optics and acoustics are reviewed. First, the fundamental models of AUV formations, which are the basis for the design of intelligent control methods when considering communication and positioning constraints, are summarized. Second, the technical difficulties and intelligent control methods considering the communication and positioning constraints along with the research results are summarized for the optical and acoustic communication and positioning methods. Finally, the development trend of AUV formation intelligent control is analyzed and discussed. The findings presented in this paper can provide a reference for the design of AUV formation intelligent control methods and has reference significance for the theoretical research and engineering application of AUV formation.
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图 4 AUV编队通信定位时间延迟示意图
Figure 4. Diagram of time delay for AUV formation communication and positioning
图 5 AUV编队通信定位中断示意图
Figure 5. Diagram of AUV formation communication and positioning interruption
表 1 AUV编队智能控制总结
Table 1. Summary of intelligent control of AUV formation
文献 通信定位方式 编队应用场景 问题特征 使用方法 验证形式 试验成果 [13] 光学通信定位 水下充电和信息交互、区域残骸探测、石油管道同步监测、珊瑚礁同步监测等 相对位姿难以准确测量 高斯拉普拉斯算子算法 2台AUV在湖中进行队形变换(对接)试验 最大的位置误差为9.818 mm [14] 相对位姿难以准确测量 卡尔曼滤波法 2台AUV在水池中进行队形变换(对接)试验 纵向位置误差趋于0 mm [16] 个体之间易碰撞 智能优化分配方法 在仿真环境中, 6台AUV在3 m的区域内作队形变换 无碰撞运动 [17] 个体之间易碰撞 Lennard-Jones势能法 保持队形的6条机器鱼在水箱中实现了半径为357 mm的同相位差圆周运动 无碰撞运动 [24~29] 声学通信定位 海底调查和测绘、资源勘探、水下考古勘探、水下救援等 时延信息可以获得 状态反馈法 在仿真环境中, 6台AUV在0.5 s的控制周期下进行队形保持和变换 系统可容忍0.4 s通信定位时延 [30] 时延信息可以获得 鲁棒控制法 在仿真环境中, 4台AUV在0.3 s的控制周期下进行队形保持和变换 系统可容忍0.6 s通信定位时延 [31] 时延信息可以获得 自适应控制法 在仿真环境中, 5台AUV在0.2 s的控制周期下进行队形保持和变换 系统可容忍5 s通信定位时延 [32] 时延信息可以获得 最优控制法 在仿真环境中, 5台AUV在0.2 s的控制周期下进行队形保持和变换 系统可容忍2 s通信定位时延 [33] 时延信息无法获得 梯度下降法 在水池中, 3台AUV在0.2 s的控制周期下进行队形保持和变换 系统可容忍5 s通信定位时延 [34] 时延信息无法获得 核密度方法估计法 在湖中, 3台AUV在5 s的控制周期下进行队形保持和变换 系统可容忍4 s通信定位时延 [34] 通信中断 曲线拟合法 在湖中, 3台AUV在5 s的控制周期下进行队形保持和变换 系统可容忍30%丢包率 [35] 通信中断 改进的广义预测控制法 在仿真环境中, AUV编队在0.1 s的控制周期下进行队形保持和变换 系统可容忍0.7 s通信定位中断 [36] 通信中断 改进的视线法 在湖中, 2台AUV在20 s的控制周期下进行队形保持和变换 系统可容忍3%的丢包率 [37] 通信中断 滑模控制器和观测器联合控制 在仿真环境中, 3台AUV在0.1 s的控制周期下进行队形保持和变换 系统可容忍0.6 s通信定位中断 
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