Advancing Ocean Observation with an AI-Driven Mobile Robotic Explorer

Abstract

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 highresolution 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.