Thom Maughan

Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters

We extend existing oceanographic sampling methodologies to sample an advecting feature of interest using autonomous robotic platforms. GPS-tracked Lagrangian drifters are used to tag and track a water patch of interest with position updates provided periodically to an autonomous underwater vehicle (AUV) for surveys around the drifter as it moves with ocean currents. Autonomous sampling methods currently rely on geographic waypoint track-line surveys that are suitable for static or slowly changing features. When studying dynamic, rapidly evolving oceanographic features, such methods at best introduce error through insufficient spatial and temporal resolution, and at worst, completely miss the spatial and temporal domain of interest. We demonstrate two approaches for tracking and sampling of advecting oceanographic features. The first relies on extending static-plan AUV surveys (the current state-of-the-art) to sample advecting features. The second approach involves planning of surveys in the drifter or patch frame of reference. We derive a quantitative envelope on patch speeds that can be tracked autonomously by AUVs and drifters and show results from a multi-day off-shore field trial. The results from the trial demonstrate the applicability of our approach to long-term tracking and sampling of advecting features. Additionally, we analyze the data from the trial to identify the sources of error that affect the quality of the surveys carried out. Our work presents the first set of experiments to autonomously observe advecting oceanographic features in the open ocean.

Simultaneous Tracking and Sampling of Dynamic Oceanographic Features with Autonomous Underwater Vehicles and Lagrangian Drifters

Studying ocean processes often requires observations made in a Lagrangian frame of reference, that is, a frame of reference moving with a feature of interest [1]. Often, the only way to understand a process is to acquire measurements at sufficient spatial and temporal resolution within a specific feature while it is evolving. Examples of coastal ocean features whose study requires Lagrangian observations include concentrated patches of microscopic algae (Fig. 1) that are toxic and may have impacts on fisheries, marine life and humans, or a patch of low-oxygen water that may cause marine life mortality depending on its movement and mixing.

Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned

With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.

ODSS: A decision support system for ocean exploration

We have designed, built, tested and fielded a decision support system which provides a platform for situational awareness, planning, observation, archiving and data analysis. While still in development, our inter-disciplinary team of computer scientists, engineers, biologists and oceanographers has made extensive use of our system in at-sea experiments since 2010. The novelty of our work lies in the targeted domain, its evolving functionalities that closely tracks how ocean scientists are seeing the evolution of their own work practice, and its actual use by engineers, scientists and marine operations personnel. We describe the architectural elements and lessons learned over the more than two years use of the system.

An experimental momentum-based front detection method for autonomous underwater vehicles

Fronts have been recognized as hotspots of intense biological activity and are important targets for observation to understand coastal ecology and transport in a changing ocean. With high spatial and temporal variability, detection and event response for frontal zones is challenging for robotic platforms like autonomous underwater vehicles (AUVs). These vehicles have shown their versatility and cost-effectiveness in using automated approaches to detect a range of features. Targeting them for in-situ observation and sampling capabilities for frontal zones then provides an important tool for characterizing rapid and episodic changes. We introduce a novel momentum-based front detection (MBFD) algorithm which utilizes a Kalman filter and a momentum accumulator function to identify significant temperature gradients associated with upwelling fronts. MBFD is designed to work at a number of levels including onboard an AUV, on-shore with a sparse real-time data stream and post-experiment on a full resolution data set gathered by a vehicle. Such a multi-layered approach plays an important role in mixed human-robot decision making for oceanographers making coordinated sampling and asset allocation strategies in large multi-robot field experiments in the coastal ocean.

Exploring Space-Time Tradeoffs in Autonomous Sampling for Marine Robotics

In the coastal ocean, biological and physical dynamics vary on spatiotemporal scales spanning many orders of magnitude. At large spatial (O(100km)) and temporal (O(weeks to months)) scales, traditional shipboard and moored measurements are very effective at quantifying mean and varying oceanic properties. At scales smaller than the internal Rossby radius (O(10km) for typical coastal stratification at mid-latitude), horizontal, vertical and temporal inhomogeneity is the rule rather than the exception.

Cabled instrument technologies for ocean acidification research — FOCE (free ocean CO 2 enrichment)

With rising concern over the impacts of ocean acidification on marine life there is a need for greatly improved techniques for carrying out in situ experiments. These must be able to create a ΔpH of 0.3 to 0.5 by addition of CO<inf>2</inf> for studies of natural ecosystems such as coral reefs, cold water corals, and other sensitive benthic habitats. Thus controlled CO<inf>2</inf> perturbation experiments in the field rather than in aquaria are quickly becoming an essential ocean science tool. Free Air CO<inf>2</inf> Enrichment (FACE) experiments have long been carried out on land to investigate the effects of elevated atmospheric CO<inf>2</inf> levels on vegetation. However, only limited work on CO<inf>2</inf> enrichment using quasi-open systems has yet been carried out in the ocean. Seawater CO<inf>2</inf> has complex chemistry with significantly slow reaction kinetics, unlike land-air experiments where simple mixing is the major concern. Ocean experimental designs must to take these reaction rates into account. The net result of adding a small quantity of CO<inf>2</inf> to seawater is to reduce the concentration of dissolved carbonate ion, and increase bicarbonate ion through the reaction: CO<inf>2</inf> + H<inf>2</inf>O + CO<inf>3</inf>²⁻ → 2HCO<inf>3</inf>⁻ The reaction between CO<inf>2</inf> and H<inf>2</inf>O is slow and is a complex function of temperature, pH, and TCO<inf>2</inf>. The reaction proceeds more rapidly at lower pH and higher temperatures. Marine animals in the natural ocean will typically experience only small and temporary shifts from environmental CO<inf>2</inf> equilibrium. Valid perturbation experiments must try to expose an experimental region to a near stable lower pH condition, and avoid large and rapid pH variability to the extent possible. This paper describes the design, development and testing of an in situ pH perturbation experiment deployed on a subsea cable for control. The paper addresses the differences between the deep-sea and shallow water versions of the experiments and addresses the pH sensor developments that enable long deployments.

Autonomous front tracking by a Wave Glider

Coastal upwelling brings cooler, saltier, and nutrient-rich deep water upward to the surface. Upwelling fronts support enriched phytoplankton and zooplankton populations, thus having great influences on ocean ecosystems. We have developed a method to enable a Wave Glider (an autonomous surface vehicle) to autonomously detect and track an upwelling front. Unlike an autonomous underwater vehicle (AUV) which runs on a yo-yo trajectory to measure vertical profiles of water properties, a Wave Gliders measurements are confined to the surface (from the “float”) and a fixed depth of only several meters (from the submerged “glider”). However, an upwelling front presents a strong surface signature that a Wave Glider can detect. Because the upwelling process brings up cold water from depth, surface temperature in an upwelling region is considerably lower than that in stratified water. A Wave Glider can detect the upwelling front based on the horizontal gradient of the near-surface temperature. We have tested the algorithm by using previous AUV data (only using near-surface temperature measurements) and Wave Glider data. We plan to run field experiments in the summer of 2016 and report the results in the presentation.