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

Abstract / Description of output

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)
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
ISBN (Electronic)	9781509063413
DOIs	https://doi.org/10.1109/MLSP.2017.8168137
Publication status	Published - 7 Dec 2017
Event	IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP): MLSP 2017 - Tokyo, Tokyo, Japan Duration: 25 Sept 2017 → 28 Jan 2018 https://signalprocessingsociety.org/blog/mlsp-2017-2017-ieee-international-workshop-machine-learning-signal-processing

Conference

Conference	IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Country/Territory	Japan
City	Tokyo
Period	25/09/17 → 28/01/18
Internet address	https://signalprocessingsociety.org/blog/mlsp-2017-2017-ieee-international-workshop-machine-learning-signal-processing

Access to Document

10.1109/MLSP.2017.8168137Licence: All Rights Reserved

https://ieeexplore.ieee.org/document/8168137Licence: All Rights Reserved

Cite this

@inproceedings{6d68e21dcb324fc5aebb176124df62a1,

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",

pages = "1--6",

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

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) : MLSP 2017 ; Conference date: 25-09-2017 Through 28-01-2018",

url = "https://signalprocessingsociety.org/blog/mlsp-2017-2017-ieee-international-workshop-machine-learning-signal-processing",

}

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). Institute of Electrical and Electronics Engineers, United States, pp. 1-6, IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan, 25/09/17. https://doi.org/10.1109/MLSP.2017.8168137

Navigation-based learning for survey trajectory classification in autonomous underwater vehicles. / De Lucas Alvarez, Mariela; Hastie, Helen; Lane, David.
2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). United States: Institute of Electrical and Electronics Engineers, 2017. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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

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AU - Hastie, Helen

AU - Lane, David

PY - 2017/12/7

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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.

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DO - 10.1109/MLSP.2017.8168137

M3 - Conference contribution

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EP - 6

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

PB - Institute of Electrical and Electronics Engineers

CY - United States

T2 - IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

Y2 - 25 September 2017 through 28 January 2018

ER -

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

Abstract / Description of output

Conference

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