PIDolan, John M.OrgCarnegie Mellon University
Award #AIST-05-0086Managing CenterGSFCStatusOpen
TitleTelesupervised Adaptive Ocean Sensor Fleet

Earth science research must bridge the gap between the atmosphere and the ocean to foster un-derstanding 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 local-ized 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 propose a telesupervision architecture that supports adap-tive reconfiguration based on environmental sensor inputs (┐smart┐ sensing), increasing data-gathering effectiveness and science return while reducing demands on scientists for tasking, con-trol, and monitoring.

We will autonomously reposition smart sensors for HAB study (initially simulated with rhoda-mine dye) by networking a fleet of NOAA surface autonomous vehicles. In-situ measurements will intelligently modify the search for areas of high concentration. Inference Grid techniques will support sensor fusion and analysis. Telesupervision will support 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. We will integrate technologies ranging from TRL 4 into a complete system and reach TRL 6 within two years. In the third year, we will advance the system to TRL 7. This system is broadly appli-cable to ecological forecasting, water management, carbon management, disaster management, coastal management, homeland security, and planetary exploration.