论文标题
在完整的DVL中断场景中，用于AUV速度预测的Set-Transformer Beamsnet
Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios
论文作者
论文摘要
自动水下车辆（AUV）定期用于深海应用。通常，自主导航任务是通过两个传感器之间的融合来执行的：惯性导航系统和多普勒速度日志（DVL）。 DVL通过将四个原声束传输到海底，然后反射后，就可以估算AUV速度向量。但是，在现实生活中，例如不均匀的海床，海洋生物阻碍了DVL的视野，并且，滚动/俯仰动作，声学梁的反射导致了一种称为DVL中断的情况。因此，速度更新无法绑定惯性解决方案漂移。为了应对这种情况，在本文中，我们利用了我们的BeamsNet框架，并提出了一个基于Set-Transformer的BeamsNet（ST-Beamsnet），该梁（ST型束）利用惯性数据读数和以前的DVL速度测量值在完整的DVL Outage中进行当前的AUV速度。使用Snapir AUV在地中海中持有的实验的数据评估了所提出的方法，并将其与移动平均值（MA）估计器进行了比较。我们的ST梁网估计速度误差为8.547％的AUV速度向量，比MA方法好26％。
Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach.
