Autonomous underwater vehicle (AUV) plays an important role in ocean research and exploration. The underwater environment has a great influence on AUV control and human–robot interaction, since underwater environment is highly dynamic with unpredictable fluctuation of water flow, high pressure and light attenuation. The traditional control model contains a large number of parameters, which is not effective and produces errors frequently. The proposal of fuzzy control addressed this issue to a certain extent. It applies fuzzy variables to the controller, which replace the values in an interval. In addition to the controller, underwater human–robot interaction is also difficult. Divers cannot speak or show any facial expressions underwater. The buttons on the AUV also need to overcome the huge water pressure. In this paper, we proposed a method to recognize the gesture instructions and apply it to the fuzzy control of AUV. Our contribution is the gesture recognition framework for the human–robot interaction, including the gesture detection network and the algorithm for the control of AUV. The experiment result shows the efficiency of the proposed method.

自主水下航行器（AUV）在海洋研究和探索中发挥着重要作用。水下环境对AUV控制和人机交互具有很大的影响，因为水下环境是高度动态的，水流，高压和光衰减无法预测。传统控制模型包含大量参数，这些参数无效且经常产生错误。模糊控制的提议在一定程度上解决了这个问题。它将模糊变量应用于控制器，该变量将替换一个间隔中的值。除控制器外，水下人机交互也很困难。潜水员无法在水下说话或显示任何面部表情。AUV上的按钮也需要克服巨大的水压。在本文中，我们提出了一种识别手势指令的方法，并将其应用于水下机器人的模糊控制。我们的贡献是用于人机交互的手势识别框架，包括手势检测网络和用于AUV控制的算法。实验结果表明了该方法的有效性。