Advancing Ocean Observation with an AI-driven Mobile Robotic Explorer

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.