Runtime Energy Estimation and Route Optimization for Autonomous Underwater Vehicles

Research output: Contribution to journalArticle

Abstract

This paper is focused on improving the self-awareness
of autonomous underwater vehicles (AUVs) operating in unknown
environments. A runtime estimation framework is introduced to
derive energy usage and navigation performance metrics in the
presence of external disturbances, such as slowly varying sea currents.
These are calculated by a state-of-the-art nonlinear regression
algorithm (LWPR) using measurements commonly available
on-board modern AUVs without relying on external sensors or a
priori knowledge about the environment. The proposed framework
is validated on two vehicles, an IVER3 AUV and a Nessie VII AUV,
in the context of real sea trials with no modification required for
the AUVs or their missions. Derived metrics are used to estimate
the feasibility of underwater missions employing the concept of
probability of mission completion (PoMC). If environmental effects
modify the vehicle’s effectiveness, a mission plan update is
performed. This is based on an energy-aware route optimization algorithm
that is also introduced in the paper. This algorithm, known
as energy-aware orienteering problem (EA-OP), shows a practical
usage for the runtime metrics. It allows an AUV to optimize its
navigation and to maximize its mission’s outcome according to
measured performances. Simulation results are also presented for
inspection scenarios. These show average improvements of 5%–
20% for the mission’s outcome when using the proposed strategy
in the presence of environmental disturbances.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Journal of Oceanic Engineering
Early online date9 Jun 2017
DOIs
Publication statusE-pub ahead of print - 9 Jun 2017

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Autonomous underwater vehicles
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Cite this

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title = "Runtime Energy Estimation and Route Optimization for Autonomous Underwater Vehicles",
abstract = "This paper is focused on improving the self-awarenessof autonomous underwater vehicles (AUVs) operating in unknownenvironments. A runtime estimation framework is introduced toderive energy usage and navigation performance metrics in thepresence of external disturbances, such as slowly varying sea currents.These are calculated by a state-of-the-art nonlinear regressionalgorithm (LWPR) using measurements commonly availableon-board modern AUVs without relying on external sensors or apriori knowledge about the environment. The proposed frameworkis validated on two vehicles, an IVER3 AUV and a Nessie VII AUV,in the context of real sea trials with no modification required forthe AUVs or their missions. Derived metrics are used to estimatethe feasibility of underwater missions employing the concept ofprobability of mission completion (PoMC). If environmental effectsmodify the vehicle’s effectiveness, a mission plan update isperformed. This is based on an energy-aware route optimization algorithmthat is also introduced in the paper. This algorithm, knownas energy-aware orienteering problem (EA-OP), shows a practicalusage for the runtime metrics. It allows an AUV to optimize itsnavigation and to maximize its mission’s outcome according tomeasured performances. Simulation results are also presented forinspection scenarios. These show average improvements of 5{\%}–20{\%} for the mission’s outcome when using the proposed strategyin the presence of environmental disturbances.",
author = "{De Carolis}, Valerio and Brown, {Keith Edgar} and Lane, {David Michael}",
year = "2017",
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doi = "10.1109/JOE.2017.2707261",
language = "English",
pages = "1--12",
journal = "IEEE Journal of Oceanic Engineering",
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AB - This paper is focused on improving the self-awarenessof autonomous underwater vehicles (AUVs) operating in unknownenvironments. A runtime estimation framework is introduced toderive energy usage and navigation performance metrics in thepresence of external disturbances, such as slowly varying sea currents.These are calculated by a state-of-the-art nonlinear regressionalgorithm (LWPR) using measurements commonly availableon-board modern AUVs without relying on external sensors or apriori knowledge about the environment. The proposed frameworkis validated on two vehicles, an IVER3 AUV and a Nessie VII AUV,in the context of real sea trials with no modification required forthe AUVs or their missions. Derived metrics are used to estimatethe feasibility of underwater missions employing the concept ofprobability of mission completion (PoMC). If environmental effectsmodify the vehicle’s effectiveness, a mission plan update isperformed. This is based on an energy-aware route optimization algorithmthat is also introduced in the paper. This algorithm, knownas energy-aware orienteering problem (EA-OP), shows a practicalusage for the runtime metrics. It allows an AUV to optimize itsnavigation and to maximize its mission’s outcome according tomeasured performances. Simulation results are also presented forinspection scenarios. These show average improvements of 5%–20% for the mission’s outcome when using the proposed strategyin the presence of environmental disturbances.

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