Data-driven robotic sampling for marine ecosystem monitoring

Jnaneshwar Das, Frédéric Py, Julio B.J. Harvey, John P. Ryan, Alyssa Gellene, Rishi Graham, David A. Caron, Kanna Rajan, Gaurav S. Sukhatme

Research output: Contribution to journalArticlepeer-review

85 Scopus citations


Robotic sampling is attractive in many field robotics applications that require persistent collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in studies involving the collection of rock, soil, and water samples for laboratory analysis. In our test domain, marine ecosystem monitoring, detailed understanding of plankton ecology requires laboratory analysis of water samples, but predictions using physical and chemical properties measured in real-time by sensors aboard an autonomous underwater vehicle (AUV) can guide sample collection decisions. In this paper, we present a data-driven and opportunistic sampling strategy to minimize cumulative regret for batches of plankton samples acquired by an AUV over multiple surveys. Samples are labeled at the end of each survey, and used to update a probabilistic model that guides sampling during subsequent surveys. During a survey, the AUV makes irrevocable sample collection decisions online for a sequential stream of candidates, with no knowledge of the quality of future samples. In addition to extensive simulations using historical field data, we present results from a one-day field trial where beginning with a prior model learned from data collected and labeled in an earlier campaign, the AUV collected water samples with a high abundance of a pre-specified planktonic target. This is the first time such a field experiment has been carried out in its entirety in a data-driven fashion, in effect "closing the loop" on a significant and relevant ecosystem monitoring problem while allowing domain experts (marine ecologists) to specify the mission at a relatively high level.

Original languageEnglish (US)
Pages (from-to)1435-1452
Number of pages18
JournalInternational Journal of Robotics Research
Issue number12
StatePublished - Oct 30 2015
Externally publishedYes


  • Robotic sampling
  • field robots
  • learning and adaptive systems
  • machine learning
  • marine robotics

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics


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