Navigation-Based learning for survey trajectory classification in autonomous underwater vehicles

Abstract

Timeseries sensor data processing is indispensable for system monitoring. Working with autonomous vehicles requires mechanisms that provide insightful information about the status of a mission. In a setting where time and resources are limited, trajectory classification plays a vital role in mission monitoring and failure detection. In this context, we use navigational data to interpret trajectory patterns and classify them. We implement Long Short-Term Memory (LSTM) based Recursive Neural Networks (RNN) that learn the most commonly used survey trajectory patterns from surveys executed by two types of Autonomous Underwater Vehicles (AUV). We compare the performance of our network against baseline machine learning methods.