Abstract
One of the primary means of sea exploration is autonomous underwater vehicles (AUVs). To perform these tasks, AUVs must navigate the rough challenging sea environment. AUVs usually employ an inertial navigation system (INS), aided by a Doppler velocity log (DVL), to provide the required navigation accuracy. The DVL transmits four acoustic beams to the seafloor, and by measuring changes in the frequency of the returning beams, the DVL can estimate the AUV velocity vector. However, in practical scenarios, not all the beams are successfully reflected. When only three beams are available, the accuracy of the velocity vector is degraded. When fewer than three beams are reflected, the DVL cannot estimate the AUV velocity vector. This paper presents a data-driven approach, MissBeamNet, to regress the missing beams in partial DVL beam measurement cases. To that end, a deep neural network (DNN) model is designed to process the available beams along with past DVL measurements to regress the missing beams. The AUV velocity vector is estimated using the available measured and regressed beams. To validate the proposed approach, sea experiments were made with the "Snapir" AUV, resulting in an 11 hours dataset of DVL measurements. Our results show that the proposed system can accurately estimate velocity vectors in situations of missing beam measurements. Our dataset and codebase implementing the described framework is available at our GitHub repository this https URL .
Abstract (translated)
海洋探索的主要手段之一是自主水下飞行器(AUVs)。要完成这些任务,AUVs必须在粗糙、挑战性的海环境中航行。AUV通常使用惯性导航系统(INS),并借助Dopler速度日志(DVL),提供所需的导航精度。DVL将四个声波束传输到海底,并通过测量返回束的频率变化,可以估计AUV的速度向量。然而,在实际应用中,并非所有的束都成功反射。只有当三个束可用时,速度向量的准确性会降低。只有当少于三个束反射时,DVL无法估计AUV的速度向量。本文提出了一种数据驱动的方法—— Miss BeamNet,用于回归partial DVL beam测量案例中缺少的束。为此,一种深度神经网络(DNN)模型被设计来处理可用的束和过去的DVL测量,以回归缺少的束。AUV的速度向量是通过可用的测量和回归束估计的。为了验证所提出的方法,我们与“Snapir”AUV进行了海洋实验,取得了11小时的DVL测量数据集。我们的结果表明,所提出的系统可以在缺少束测量的情况下准确地估计速度向量。我们的数据集和代码库实现所述框架的可用资源在我们的GitHub存储库中。
URL
https://arxiv.org/abs/2301.11597
