An online utility-based approach for sampling dynamic ocean fields
Angel García-Olaya, Frédéric Py, Jnaneshwar Das, Kanna Rajan
The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.
IEEE Journal of Oceanic Engineering
https://doi.org/10.1109/JOE.2012.2183934
Autonomous underwater vehicles (AUVs)
10.1109/JOE.2012.2183934
Dive into the research topics of 'An online utility-based approach for sampling dynamic ocean fields'. Together they form a unique fingerprint.
Autonomous underwater vehicles Engineering & Materials Science 100%
Sampling Engineering & Materials Science 89%
Coastal zones Engineering & Materials Science 46%
Demonstrations Engineering & Materials Science 34%
García-Olaya, A., Py, F., Das, J., & Rajan, K. (2012). An online utility-based approach for sampling dynamic ocean fields. IEEE Journal of Oceanic Engineering, 37(2), 185-203. [6168799]. https://doi.org/10.1109/JOE.2012.2183934
An online utility-based approach for sampling dynamic ocean fields. / García-Olaya, Angel; Py, Frédéric; Das, Jnaneshwar; Rajan, Kanna.
In: IEEE Journal of Oceanic Engineering, Vol. 37, No. 2, 6168799, 04.2012, p. 185-203.
García-Olaya, A, Py, F, Das, J & Rajan, K 2012, 'An online utility-based approach for sampling dynamic ocean fields', IEEE Journal of Oceanic Engineering, vol. 37, no. 2, 6168799, pp. 185-203. https://doi.org/10.1109/JOE.2012.2183934
García-Olaya A, Py F, Das J, Rajan K. An online utility-based approach for sampling dynamic ocean fields. IEEE Journal of Oceanic Engineering. 2012 Apr;37(2):185-203. 6168799. https://doi.org/10.1109/JOE.2012.2183934
García-Olaya, Angel ; Py, Frédéric ; Das, Jnaneshwar ; Rajan, Kanna. / An online utility-based approach for sampling dynamic ocean fields. In: IEEE Journal of Oceanic Engineering. 2012 ; Vol. 37, No. 2. pp. 185-203.
@article{1aae925d03bb49eb99f830266e5bfb4b,
title = "An online utility-based approach for sampling dynamic ocean fields",
abstract = "The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.",
keywords = "Autonomous underwater vehicles (AUVs), autonomy, sampling",
author = "Angel Garc{\'i}a-Olaya and Fr{\'e}d{\'e}ric Py and Jnaneshwar Das and Kanna Rajan",
note = "Funding Information: Manuscript received February 16, 2011; revised September 12, 2011; accepted January 08, 2012. Date of publication March 13, 2012; date of current version April 13, 2012. The work of A. Garc{\'i}a-Olaya was supported in part by the Spanish Government (MICIIN) under Project TIN2008-06701-C03-03 and by the Madrid Region Government under Project CCG10-UC3M/TIC-5597. The work of J. Das was supported in part by the National Oceanic and Atmospheric Administration (NOAA) Monitoring and Event Response for Harmful Algal Blooms (MERHAB) program under Grant NA05NOS4781228, by the National Science Foundation (NSF) as part of the Center for Embedded Networked Sensing (CENS) under Grant CCR-0120778, by the NSF under Grants CNS-0520305 and CNS-0540420, by the U.S. Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) program under Grants N00014-09-1-1031 and N00014-08-1-0693. The work of F. Py and K. Rajan was supported by a block grant from the Packard Foundation to the Monterey Bay Aquarium Research Institute (MBARI). Associate Editor: R. Eustice.",
doi = "10.1109/JOE.2012.2183934",
journal = "IEEE Journal of Oceanic Engineering",
T1 - An online utility-based approach for sampling dynamic ocean fields
AU - García-Olaya, Angel
AU - Py, Frédéric
AU - Das, Jnaneshwar
AU - Rajan, Kanna
N1 - Funding Information: Manuscript received February 16, 2011; revised September 12, 2011; accepted January 08, 2012. Date of publication March 13, 2012; date of current version April 13, 2012. The work of A. García-Olaya was supported in part by the Spanish Government (MICIIN) under Project TIN2008-06701-C03-03 and by the Madrid Region Government under Project CCG10-UC3M/TIC-5597. The work of J. Das was supported in part by the National Oceanic and Atmospheric Administration (NOAA) Monitoring and Event Response for Harmful Algal Blooms (MERHAB) program under Grant NA05NOS4781228, by the National Science Foundation (NSF) as part of the Center for Embedded Networked Sensing (CENS) under Grant CCR-0120778, by the NSF under Grants CNS-0520305 and CNS-0540420, by the U.S. Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) program under Grants N00014-09-1-1031 and N00014-08-1-0693. The work of F. Py and K. Rajan was supported by a block grant from the Packard Foundation to the Monterey Bay Aquarium Research Institute (MBARI). Associate Editor: R. Eustice.
N2 - The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.
AB - The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.
KW - Autonomous underwater vehicles (AUVs)
KW - autonomy
KW - sampling
U2 - 10.1109/JOE.2012.2183934
DO - 10.1109/JOE.2012.2183934
JO - IEEE Journal of Oceanic Engineering
JF - IEEE Journal of Oceanic Engineering