Advancing Ocean Observation with an AI-driven Mobile Robotic Explorer
Saad, Aya; Stahl, Annette; Våge, Andreas; Davies, Emlyn John; Nordam, Tor; Aberle-Malzahn, Nicole; Ludvigsen, Martin; Johnsen, Geir; Sousa, João; Rajan, Kanna
Journal article, Peer reviewed
Published version
View/ Open
Date
2020Metadata
Show full item recordCollections
- Institutt for biologi [2512]
- Institutt for marin teknikk [3397]
- Institutt for teknisk kybernetikk [3658]
- Publikasjoner fra CRIStin - NTNU [37177]
Abstract
Rapid assessment and enhanced knowledge of plankton communities and their structure in the productive upper water column is of crucial importance to understand the impact of the changing climate on upper ocean processes. Enabling persistent and systematic observation by coupling the ongoing revolution in robotics and automation with artificial intelligence methods will improve accuracy of predictions, reduce measurement uncertainty and accelerate methodological sampling. 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 for characterizing upper water-column biota, enabling intelligent onboard sampling to target specific mesoplankton taxa. Such a tool will accelerate the time consuming process in asking ``who is there'' and aid the advancement of oceanographic observation.