Title of Presentation: Smart Ocean Sensing Using the Telesupervised Adaptive Ocean Sensor Fleet

Primary (Corresponding) Author: John Dolan

Organization of Primary Author: Carnegie Mellon University

Co-Authors: Gregg Podnar, Steve Stancliff, Ellie Lin Ratliff, Alberto Elfes, John HIginbotham, Jeff Hosler, John Moisan, Tiffany Moisan

Abstract: This paper describes initial field test results using a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended-deployment autonomous ocean surface vessels, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.