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

Mariela De Lucas Alvarez; Helen Hastie; David Lane

doi:10.1109/MLSP.2017.8168137

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 proceeding › Conference contribution

3 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 language	English
Title of host publication	2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Publisher	IEEE
ISBN (Electronic)	9781509063413
DOIs	https://doi.org/10.1109/MLSP.2017.8168137
Publication status	Published - 7 Dec 2017
Event	2017 IEEE 27th International Workshop on Machine Learning for Signal Processing - Tokyo, Japan Duration: 25 Sept 2017 → 28 Sept 2017

Conference

Conference	2017 IEEE 27th International Workshop on Machine Learning for Signal Processing
Abbreviated title	MLSP 2017
Country/Territory	Japan
City	Tokyo
Period	25/09/17 → 28/09/17

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10.1109/MLSP.2017.8168137

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@inproceedings{b041d6aacf4644c6ba737f4e3c4fd45b,

title = "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.",

author = "{De Lucas Alvarez}, Mariela and Helen Hastie and David Lane",

year = "2017",

month = dec,

day = "7",

doi = "10.1109/MLSP.2017.8168137",

language = "English",

booktitle = "2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE 27th International Workshop on Machine Learning for Signal Processing, MLSP 2017 ; Conference date: 25-09-2017 Through 28-09-2017",

}

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, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing, Tokyo, Japan, 25/09/17. https://doi.org/10.1109/MLSP.2017.8168137

TY - GEN

T1 - Navigation-based learning for survey trajectory classification in autonomous underwater vehicles

AU - De Lucas Alvarez, Mariela

AU - Hastie, Helen

AU - Lane, David

PY - 2017/12/7

Y1 - 2017/12/7

N2 - 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.

AB - 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.

U2 - 10.1109/MLSP.2017.8168137

DO - 10.1109/MLSP.2017.8168137

M3 - Conference contribution

BT - 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

PB - IEEE

T2 - 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing

Y2 - 25 September 2017 through 28 September 2017

ER -

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

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