Autonomous Underwater Vehicles (AUVs) are platforms used for research and exploration of marine environments. However, these types of vehicles face many challenges that hinder their widespread use in the industry. One of the main limitations is obtaining accurate position estimation, due to the lack of GPS signal underwater. This estimation is usually done with Kalman filters. However, new developments in the neuroscience field have shed light on the mechanisms by which mammals are able to obtain a reliable estimation of their current position based on external and internal motion cues. A new type of neuron, called Grid cells, has been shown to be part of path integration system in the brain. In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles. The model of grid cells used requires only the linear velocities together with heading orientation and provides a reliable estimation of the vehicle's position. We provide simulation results for an AUV which show the feasibility of our proposed methodology.

中文翻译：

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使用网格单元进行路径集成的船舶定位

自主水下航行器 (AUV) 是用于研究和探索海洋环境的平台。然而，这些类型的车辆面临许多阻碍其在行业中广泛使用的挑战。由于在水下缺乏 GPS 信号，主要限制之一是获得准确的位置估计。这种估计通常使用卡尔曼滤波器完成。然而，神经科学领域的新发展揭示了哺乳动物能够根据外部和内部运动线索获得对其当前位置的可靠估计的机制。一种称为网格细胞的新型神经元已被证明是大脑路径整合系统的一部分。在本文中，我们展示了如何使用网格单元来获得水下航行器的位置估计。所使用的网格单元模型只需要线速度和航向方向，并提供对车辆位置的可靠估计。我们提供了 AUV 的模拟结果，显示了我们提出的方法的可行性。