Adaptive Sampling of Transient Environmental Phenomena with Autonomous Mobile Platforms

Victoria Preston, S.M., 2019
Anna Michel, Co-Advisor
Nicholas Roy, Co-Advisor
In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis.
Logistical challenges in collecting, storing, and analyzing these samples limit the overall efficacy of these methods. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon 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 human-selected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions. A skilled human expert 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 and carbon dioxide 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 requires a mobile vehicle to efficiently find and densely sample from the most scientifically valuable region in an a priori unknown, dynamic environment. The PLUMES algorithm – Plume Localization under Uncertainty using Maximum-valuE information and Search – is subsequently presented, which addresses the MSS problem and overcomes key technical challenges with planning in natural environments. Theoretical performance guarantees are derived for PLUMES , and empirical performance is demonstrated against canonical uniform search and state-of-the-art baselines in simulation and field trials.