The telesupervised adaptive ocean sensor fleet

Elfes, Alberto; Podnar, Gregg; Dolan, John M.; Stancliff, Stephen B.; Lin, Ellie; Hosler, Jeffrey; Ames, Troy; Moisan, John R.; Moisan, Tiffany; Higinbotham, John; Kulczycki, Eric

doi:10.1117/12.735561

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…Because some of the TAOSF system testing and validation is done using an aerial imaging platform (discussed later), it became necessary to correlate aerial dye visibility to dye concentration in the water. This was done by imaging dye diffusion and visibility in a local pond in Pittsburgh [12].…”

Section: Visual Rhodamine Dye Mappingmentioning

confidence: 99%

The Telesupervised Adaptive Ocean Sensor Fleet Architecture

Elfes

Podnar

Dolan

et al. 2008

2008 IEEE Aerospace Conference

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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. 12

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Section: Visual Rhodamine Dye Mappingmentioning

confidence: 99%

The Telesupervised Adaptive Ocean Sensor Fleet Architecture

Elfes

Podnar

Dolan

et al. 2008

2008 IEEE Aerospace Conference

Self Cite

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“…We used a Gaussian process approach to predict the cell counts of the dinoflagellate Karlodinium micrum from water quality features (temperature, salinity, and dissolved oxygen). The results are beyond the scope of this paper, and are discussed in [7,8]. We also investigated an adaptive sampling approach using the Regional Ocean Modeling System (ROMS) model of the Chesapeake Bay using Gaussian processes to select positions for obtaining sensor measurements to optimally characterize the distribution of salinity from known temperature data [7,8].…”

Section: Hab Sensing and Characterizationmentioning

confidence: 99%

“…Our work in this area has two components [7,8]. First, we are assembling and analyzing all known HAB-related data from the Chesapeake Bay area.…”

Section: Hab Sensing and Characterizationmentioning

confidence: 99%

“…Surface water samples were taken of each patch at measured time intervals and were analyzed using the same fluorometers used on the OASIS platform. This allowed us to establish a baseline for correlating camera visibility with in-water concentration measurements [7,8].…”

Section: Hab Sensing and Characterizationmentioning

confidence: 99%

“…To address this need, we developed an aerial observation system carrying an avionics package that includes GPS, barometric altimeter, magnetic compass, serial data link, wide-angle color camera, and video transmitter [7,8]. Fig.…”

Section: Aerial Field Test Observation Platformmentioning

confidence: 99%

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Operation of Robotic Science Boats Using the Telesupervised Adaptive Ocean Sensor Fleet System

Podnar

Dolan

Elfes

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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-This paper describes 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 datagathering 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.

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Development of a Low Cost Multi-Robot Autonomous Marine Surface Platform

Valada

Velagapudi

Kannan

et al. 2013

Springer Tracts in Advanced Robotics

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In this paper, we outline a low cost multi-robot autonomous platform for a broad set of applications including water quality monitoring, flood disaster mitigation and depth buoy verification. By working cooperatively, fleets of vessels can cover large areas that would otherwise be impractical, time consuming and prohibitively expensive to traverse by a single vessel. We describe the hardware design, control infrastructure, and software architecture of the system, while additionally presenting experimental results from several field trials. Further, we discuss our initial efforts towards developing our system for water quality monitoring, in which a team of watercraft equipped with specialized sensors autonomously samples the physical quantity being measured and provides online situational awareness to the operator regarding water quality in the observed area. From canals in New York to volcanic lakes in the Philippines, our vessels have been tested in diverse marine environments and the results obtained from initial experiments in these domains are also discussed.

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The telesupervised adaptive ocean sensor fleet

Cited by 3 publications

References 4 publications

The Telesupervised Adaptive Ocean Sensor Fleet Architecture

The Telesupervised Adaptive Ocean Sensor Fleet Architecture

Operation of Robotic Science Boats Using the Telesupervised Adaptive Ocean Sensor Fleet System

Development of a Low Cost Multi-Robot Autonomous Marine Surface Platform

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