Towards marine bloom trajectory prediction for AUV mission planning

Jnaneshwar Das, Kanna Rajan, Sergey Frolov, Frederic Py, John Ryan, David A. Caron, Gaurav S. Sukhatme

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)

Abstract

This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the 'right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Pages4784-4790
Number of pages7
DOIs
StatePublished - Aug 26 2010
Externally publishedYes
Event2010 IEEE International Conference on Robotics and Automation, ICRA 2010 - Anchorage, AK, United States
Duration: May 3 2010May 7 2010

Other

Other2010 IEEE International Conference on Robotics and Automation, ICRA 2010
CountryUnited States
CityAnchorage, AK
Period5/3/105/7/10

Fingerprint

Radar stations
Trajectories
Sampling
Planning
Ecology
Remote sensing
Robotics
Radar
Satellites
Color
Water
Experiments

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Das, J., Rajan, K., Frolov, S., Py, F., Ryan, J., Caron, D. A., & Sukhatme, G. S. (2010). Towards marine bloom trajectory prediction for AUV mission planning. In 2010 IEEE International Conference on Robotics and Automation, ICRA 2010 (pp. 4784-4790). [5509930] https://doi.org/10.1109/ROBOT.2010.5509930

Towards marine bloom trajectory prediction for AUV mission planning. / Das, Jnaneshwar; Rajan, Kanna; Frolov, Sergey; Py, Frederic; Ryan, John; Caron, David A.; Sukhatme, Gaurav S.

2010 IEEE International Conference on Robotics and Automation, ICRA 2010. 2010. p. 4784-4790 5509930.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Das, J, Rajan, K, Frolov, S, Py, F, Ryan, J, Caron, DA & Sukhatme, GS 2010, Towards marine bloom trajectory prediction for AUV mission planning. in 2010 IEEE International Conference on Robotics and Automation, ICRA 2010., 5509930, pp. 4784-4790, 2010 IEEE International Conference on Robotics and Automation, ICRA 2010, Anchorage, AK, United States, 5/3/10. https://doi.org/10.1109/ROBOT.2010.5509930
Das J, Rajan K, Frolov S, Py F, Ryan J, Caron DA et al. Towards marine bloom trajectory prediction for AUV mission planning. In 2010 IEEE International Conference on Robotics and Automation, ICRA 2010. 2010. p. 4784-4790. 5509930 https://doi.org/10.1109/ROBOT.2010.5509930
Das, Jnaneshwar ; Rajan, Kanna ; Frolov, Sergey ; Py, Frederic ; Ryan, John ; Caron, David A. ; Sukhatme, Gaurav S. / Towards marine bloom trajectory prediction for AUV mission planning. 2010 IEEE International Conference on Robotics and Automation, ICRA 2010. 2010. pp. 4784-4790
@inproceedings{c4c42071b1184215a8c9651ecb749638,
title = "Towards marine bloom trajectory prediction for AUV mission planning",
abstract = "This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the 'right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.",
author = "Jnaneshwar Das and Kanna Rajan and Sergey Frolov and Frederic Py and John Ryan and Caron, {David A.} and Sukhatme, {Gaurav S.}",
year = "2010",
month = "8",
day = "26",
doi = "10.1109/ROBOT.2010.5509930",
language = "English (US)",
isbn = "9781424450381",
pages = "4784--4790",
booktitle = "2010 IEEE International Conference on Robotics and Automation, ICRA 2010",

}

TY - GEN

T1 - Towards marine bloom trajectory prediction for AUV mission planning

AU - Das, Jnaneshwar

AU - Rajan, Kanna

AU - Frolov, Sergey

AU - Py, Frederic

AU - Ryan, John

AU - Caron, David A.

AU - Sukhatme, Gaurav S.

PY - 2010/8/26

Y1 - 2010/8/26

N2 - This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the 'right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

AB - This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the 'right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

UR - http://www.scopus.com/inward/record.url?scp=77955811525&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955811525&partnerID=8YFLogxK

U2 - 10.1109/ROBOT.2010.5509930

DO - 10.1109/ROBOT.2010.5509930

M3 - Conference contribution

AN - SCOPUS:77955811525

SN - 9781424450381

SP - 4784

EP - 4790

BT - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010

ER -