The design of a robust perception method is a substantial component towards achieving underwater human–robot collaboration. However, in complex environments such as the oceans, perception is still a challenging issue. Data‐fusion of different sensing modalities can improve perception in dynamic and unstructured ocean environments. This study addresses the control of a highly maneuverable autonomous underwater vehicle for diver tracking based on visual and acoustic signals data fusion measured by low‐cost sensors. The underwater vehicle U‐CAT tracks a diver using a 3‐degree‐of‐freedom fuzzy logic Mamdani controller. The proposed tracking approach was validated through open water real‐time experiments. Combining acoustic and visual signals for underwater target tracking provides several advantages compared to previously done related research. The obtained results suggest that the proposed solution ensures effective detection and tracking in poor visibility operating conditions.

中文翻译：

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在开阔水域中进行潜水员追踪：一种基于视觉和声音传感器融合的低成本方法

健壮的感知方法的设计是实现水下人机协作的重要组成部分。然而，在海洋等复杂环境中，感知仍然是一个具有挑战性的问题。不同传感方式的数据融合可以改善在动态和非结构化海洋环境中的感知能力。这项研究基于低成本传感器测量的视觉和听觉信号数据融合，探讨了一种用于潜水员跟踪的机动性强的水下机器人的控制方法。水下车辆U‐CAT使用3自由度模糊逻辑Mamdani控制器跟踪潜水员。通过公开水域实时实验对提出的跟踪方法进行了验证。与先前进行的相关研究相比，将声音和视觉信号相结合以进行水下目标跟踪具有许多优势。