TY - GEN
T1 - Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena
AU - Das, Jnaneshwar
AU - Harvey, Julio
AU - Py, Frederic
AU - Vathsangam, Harshvardhan
AU - Graham, Rishi
AU - Rajan, Kanna
AU - Sukhatme, Gaurav S.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84887268683&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887268683&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2013.6631377
DO - 10.1109/ICRA.2013.6631377
M3 - Conference contribution
AN - SCOPUS:84887268683
SN - 9781467356411
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5571
EP - 5578
BT - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013
T2 - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Y2 - 6 May 2013 through 10 May 2013
ER -