Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena

Jnaneshwar Das, Julio Harvey, Frederic Py, Harshvardhan Vathsangam, Rishi Graham, Kanna Rajan, Gaurav S. Sukhatme

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

14 Scopus citations

Abstract

Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Pages5571-5578
Number of pages8
DOIs
StatePublished - Nov 14 2013
Externally publishedYes
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: May 6 2013May 10 2013

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013
CountryGermany
CityKarlsruhe
Period5/6/135/10/13

ASJC Scopus subject areas

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

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