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首页> 外文期刊>Ocean Engineering >End-to-end navigation for Autonomous Underwater Vehicle with Hybrid Recurrent Neural Networks
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End-to-end navigation for Autonomous Underwater Vehicle with Hybrid Recurrent Neural Networks

机译：具有混合递归神经网络的自动水下航行器的端到端导航

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摘要

This paper presents a novel navigation method for Autonomous Underwater Vehicle (AUV). In respect of improving the precision of navigation, recent research mainly focused on how to reduce the system model error. However, existing optimization methods are less efficient and effective in decreasing interference of sensor deviation. Motivated by the excellent performance of deep-learning, a Hybrid Recurrent Neural Networks (Hybrid RNNs) framework is proposed to estimate the AUV position. Firstly, since the different sensors have different data frequency, this method employs unidirectional and bi-directional long short-term memory (LSTM) with multiple memory units to handle raw sensor values in a single calculation cycle. Subsequently, using the outputs of LSTMs and the time interval of the cycle above, the fully connected layers could obtain the displacements of AUV. Eventually, to verify the effectiveness of the proposed navigation algorithm, a series of evaluations have been carried out, which are based on a public dataset and real experimental data of our AUV. The evaluation results have been validated that the proposed method can reduce the interference of sensor deviation, and has better accuracy as well as fault tolerance for navigation. Meanwhile, it could also satisfy the real-time requirement.
机译：本文提出了一种新的自主水下航行器导航方法。在提高导航精度方面，最近的研究主要集中在如何减少系统模型误差上。但是，现有的优化方法在降低传感器偏差的干扰方面效率较低。由于深度学习的卓越性能，提出了一种混合递归神经网络（Hybrid RNNs）框架来估计AUV位置。首先，由于不同的传感器具有不同的数据频率，因此该方法采用具有多个存储单元的单向和双向长短期存储器（LSTM），以在单个计算周期中处理原始传感器值。随后，使用LSTM的输出和上述周期的时间间隔，完全连接的层可以获得AUV的位移。最终，为了验证所提出的导航算法的有效性，已经进行了一系列评估，这些评估基于公开数据集和我们AUV的真实实验数据。评估结果表明，该方法可以减少传感器偏差的干扰，具有较好的精度和导航的容错能力。同时，它也可以满足实时性要求。

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