A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring
This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a n...
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ftmdpi:oai:mdpi.com:/1424-8220/20/6/1614/ 2023-08-20T04:07:53+02:00 A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring Jacopo Aguzzi Jan Albiez Sascha Flögel Olav Rune Godø Endre Grimsbø Simone Marini Olaf Pfannkuche Erik Rodriguez Laurenz Thomsen Terje Torkelsen Javier Valencia Vanesa López-Vázquez Henning Wehde Guosong Zhang 2020-03-13 application/pdf https://doi.org/10.3390/s20061614 EN eng Multidisciplinary Digital Publishing Institute Intelligent Sensors https://dx.doi.org/10.3390/s20061614 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 20; Issue 6; Pages: 1614 benthic and pelagic monitoring image processing acoustics crawler docking station cabled observatories fuel cells ecosystem component classification Text 2020 ftmdpi https://doi.org/10.3390/s20061614 2023-07-31T23:14:12Z This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining. Text Lofoten Vesterålen MDPI Open Access Publishing Lofoten Norway Vesterålen ENVELOPE(14.939,14.939,68.754,68.754) Sensors 20 6 1614 |
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Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
benthic and pelagic monitoring image processing acoustics crawler docking station cabled observatories fuel cells ecosystem component classification |
spellingShingle |
benthic and pelagic monitoring image processing acoustics crawler docking station cabled observatories fuel cells ecosystem component classification Jacopo Aguzzi Jan Albiez Sascha Flögel Olav Rune Godø Endre Grimsbø Simone Marini Olaf Pfannkuche Erik Rodriguez Laurenz Thomsen Terje Torkelsen Javier Valencia Vanesa López-Vázquez Henning Wehde Guosong Zhang A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
topic_facet |
benthic and pelagic monitoring image processing acoustics crawler docking station cabled observatories fuel cells ecosystem component classification |
description |
This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining. |
format |
Text |
author |
Jacopo Aguzzi Jan Albiez Sascha Flögel Olav Rune Godø Endre Grimsbø Simone Marini Olaf Pfannkuche Erik Rodriguez Laurenz Thomsen Terje Torkelsen Javier Valencia Vanesa López-Vázquez Henning Wehde Guosong Zhang |
author_facet |
Jacopo Aguzzi Jan Albiez Sascha Flögel Olav Rune Godø Endre Grimsbø Simone Marini Olaf Pfannkuche Erik Rodriguez Laurenz Thomsen Terje Torkelsen Javier Valencia Vanesa López-Vázquez Henning Wehde Guosong Zhang |
author_sort |
Jacopo Aguzzi |
title |
A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
title_short |
A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
title_full |
A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
title_fullStr |
A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
title_full_unstemmed |
A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring |
title_sort |
flexible autonomous robotic observatory infrastructure for bentho-pelagic monitoring |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/s20061614 |
long_lat |
ENVELOPE(14.939,14.939,68.754,68.754) |
geographic |
Lofoten Norway Vesterålen |
geographic_facet |
Lofoten Norway Vesterålen |
genre |
Lofoten Vesterålen |
genre_facet |
Lofoten Vesterålen |
op_source |
Sensors; Volume 20; Issue 6; Pages: 1614 |
op_relation |
Intelligent Sensors https://dx.doi.org/10.3390/s20061614 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s20061614 |
container_title |
Sensors |
container_volume |
20 |
container_issue |
6 |
container_start_page |
1614 |
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1774719807018500096 |