Advancing Ocean Observation with an AI-Driven Mobile Robotic Explorer

Rapid assessment and enhanced knowledge of plankton communities and their structures in the productive upper water column is of crucial importance if we are to understand the impact of the changing climate on upper ocean processes. Enabling persistent and systematic ecosystem surveillance by couplin...

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Bibliographic Details
Published in:Oceanography (Washington, D.C.) D.C.), 2020-09, Vol.33 (3), p.50-59
Main Authors: Saad, Aya, Stahl, Annette, Våge, Andreas, Davies, Emlyn, Nordam, Tor, Aberle, Nicole, Ludvigsen, Martin, Johnsen, Geir, Sousa, João, Rajan, Kanna
Format: Article
Language:English
Subjects:
Artificial intelligence
Biota
BREAKING WAVES
Climate change
Environmental changes
Hydrodynamics
Imaging techniques
Machine learning
Marine ecosystems
Plankton
Resolution
Robotics
Sampling
Submarine pipelines
Upper ocean
Water column
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Summary:Rapid assessment and enhanced knowledge of plankton communities and their structures in the productive upper water column is of crucial importance if we are to understand the impact of the changing climate on upper ocean processes. Enabling persistent and systematic ecosystem surveillance by coupling the revolution in robotics and automation with artificial intelligence (AI) methods will improve accuracy of predictions, reduce measurement uncertainty, and accelerate methodological sampling with high spatial and temporal resolution. Further, progress in real-time robotic visual sensing and machine learning have enabled high-resolution space-time imaging, analysis, and interpretation. We describe a novel mobile robotic tool that characterizes upper water column biota by employing intelligent onboard sampling to target specific mesoplankton taxa. Although we focus on machine learning techniques, we also outline the processing pipeline that combines imaging, supervised machine learning, hydrodynamics, and AI planning. The tool we describe will accelerate the time-consuming task of analyzing “who is there” and thus advance oceanographic observation.
ISSN:1042-8275
2377-617X
DOI:10.5670/oceanog.2020.307