Targeted Sampling by Autonomous Underwater Vehicles

Yanwu Zhang,Christopher A Scholin,Brett W Hobson,John P Ryan,Brian Kieft,James G Bellingham,Michael A Godin,James M Birch,Julio B Harvey,Benedetto Barone,Robert S Mcewen,Francisco P Chavez

doi:10.3389/fmars.2019.00415

Abstract

In the vast ocean, many ecologically important phenomena are temporally episodic, localized in space, and move according to local currents. To effectively study these complex and evolving phenomena, methods that enable autonomous platforms to detect and respond to targeted phenomena are required. Such capabilities allow for directed sensing and water sample acquisition in the most relevant and informative locations, as compared against static grid surveys. To meet this need, we have designed algorithms for autonomous underwater vehicles (AUVs) that detect oceanic features in real time and then direct vehicle and sampling behaviors as dictated by research objectives. These methods have successfully been applied in a series of field programs to study a range of phenomena such as harmful algal blooms, coastal upwelling fronts, and microbial processes in open-ocean eddies. In this brief review we highlight these applications and discuss future directions.

Highlights

Traditional ship-based methods for detecting and sampling dynamic ocean features are often laborious and difficult, and long-term tracking of such features using ships is practically impossible
We developed an autonomous underwater vehicles (AUVs) algorithm to autonomously distinguish between upwelling and stratified water columns based on vertical temperature homogeneity, and to accurately locate an upwelling front based on the horizontal gradient of vertical temperature homogeneity (Zhang et al, 2012b,c)
Using the vertical temperature homogeneity index (VTHI) metric, we developed an AUV algorithm for tracking a stratification front (Zhang et al, 2012b) as it moves due to variations in wind and ocean circulation

Summary

INTRODUCTION

Traditional ship-based methods for detecting and sampling dynamic ocean features are often laborious and difficult, and long-term tracking of such features using ships is practically impossible. The AUV detected the plume based on a proxy signal (e.g., optical backscatter signal for hydrothermal vent plumes) and followed a tracking strategy to trace the plume source and map the plume field. In parallel with AUV hardware developments, a long-sought goal is to develop onboard intelligence that allows the AUV to autonomously assess prevailing conditions and determine when and where to focus survey observations and water sample collections. We call this “targeted sampling”—the use of AUVs to detect specific oceanic features based on real-time analysis of sensor data, and to respond to detection through vehicle path adaptation and water sample acquisition.

AUV TARGETED SAMPLING METHODS AND FIELD EXPERIMENTS

Capturing Peak Samples in a Phytoplankton Patch

Tracking a Physical and Biological Front From Upwelling to Relaxation

CONCLUSION AND FUTURE WORK