Title of Presentation: Harmful Algal Bloom Characterization via the Telesupervised Adaptive Ocean Sensor Fleet
Primary (Corresponding) Author: John Dolan
Organization of Primary Author:
Abstract: Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to reposition. Cloud cover inhibits study of localized transient phenomena such as a Harmful Algal Bloom (HAB).
A fleet of extended-deployment surface autonomous vehicles will enable in-situ study of surface and sub-surface characteristics of HABs, coastal pollutants, oil spills, and hurricane factors. To enhance the value of these assets, we are developing a telesupervision architecture that supports adaptive reconfiguration based on environmental sensor inputs (“smart” sensing), increasing data-gathering effectiveness and science return while reducing demands on scientists for tasking, control, and monitoring.
Our system allows the autonomous repositioning of smart sensors for HAB study (initially simulated with rhodamine dye) by networking a fleet of NOAA OASIS (Ocean-Atmosphere Sensor Integration System) surface autonomous vehicles. In-situ measurements intelligently modify the search for areas of high concentration. Inference Grid techniques support sensor fusion and analysis. Telesupervision supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when direct human interaction is appropriate.
Telesupervised surface autonomous vehicles are crucial to the sensor web for Earth science. This system is broadly applicable to ecological forecasting, water management, carbon management, disaster management, coastal management, homeland security, and planetary exploration.
Keywords: harmful algal blooms, sensor web, ocean sensing, adaptive sampling, telesupervision, multirobot systems