Adaptive sampling of transient environmental phenomena with autonomous mobile platforms
type: CreativeWork
description: In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which …
abstract: In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which …
author: Preston, Victoria Lynn
language: en_US
date published: 2019-09
date modified: 2026-07-27T22:05:19Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/25090

PropertyValue
property ID: DOI
value: 10.1575/1912/25090