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

Mariela De Lucas Alvarez, Helen Hastie, David Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherIEEE
ISBN (Electronic)9781509063413
DOIs
Publication statusPublished - 7 Dec 2017
Event2017 IEEE 27th International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Duration: 25 Sep 201728 Sep 2017

Conference

Conference2017 IEEE 27th International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP 2017
CountryJapan
CityTokyo
Period25/09/1728/09/17

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  • Cite this

    De Lucas Alvarez, M., Hastie, H., & Lane, D. (2017). Navigation-based learning for survey trajectory classification in autonomous underwater vehicles. In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) [8168137] IEEE. https://doi.org/10.1109/MLSP.2017.8168137