Learning-based event response for marine robotics

Abstract

Robotic vehicles have become a critical tool for studying the under-sampled coastal ocean. This has led to new paradigms in scientific discovery. The combination of agility, reactivity, and persistent presence makes autonomous robots ideal for targeted sampling of elusive, episodic events such as algal blooms. In order to achieve this goal, they need to be deployed at the right place and time. To that end, we have designed and will soon deploy a shore-based event recognition technology to continuously monitor remote sensing imagery for algal blooms as targets for robotic field experiments. A Support Vector Machine underlies a field-tested decision support system which scientists will consult prior to deploying robots in the coastal ocean. Our aim is to target oceanographic field experiments for evaluation and verification.