Date of Original Version
Abstract or Description
Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth`s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a Multilevel Autonomy Robot Telesupervision Architecture (MARTA) for multi-robot science exploration, and an embodiment of the MARTA architecture in a real-world 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 extendeddeployment autonomous ocean surface vehicles, 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 the MARTA multi-level autonomy control architecture, where the operating mode of the vessels 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 system and the underlying MARTA 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.