In the underwater environment, spatiotemporally dynamic environmental conditions pose challenges to the detection and tracking of hydrographic features. A useful tool in combating these challenge is Autonomous Adaptive Environmental Assessment (AAEA) employed on board Autonomous Underwater Vehicles (AUVs). AAEA is a process by which an AUV autonomously assesses the hydrographic environment it is swimming through in real-time, effectively detecting hydrographic features in the area. This feature detection process leads naturally to the subsequent active/adaptive tracking of a selected feature. Due to certain restrictions in operating AUVs this detection-tracking feedback loop setup with AAEA can only rely on having an AUV's self-collected hydrographic data (e.g., temperature, conductivity, and/or pressure readings) available. With a basic quantitative definition of an underwater feature of interest, an algorithm can be developed (with which a data set is evaluated) to detect said feature. One example of feature tracking with AAEA explored in this paper is tracking the marine thermocline. The AAEA process for thermocline tracking is outlined here from quantitatively defining the thermocline region and calculating thermal gradients, all the way through simulation and implementation of the process on AUVs. Adaptation to varying feature properties, scales, and other challenges in bringing the concept of feature tracking with AAEA into implementation in field experiments is addressed, and results from two recent field experiments are presented.

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Advances in the fields of autonomy software and environmental sampling techniques for autonomous

In recent years, there has been significant concern about the impacts of offshore oil spill plumes and harmful algal blooms on the coastal ocean environment and biology, as well as on the human populations adjacent to these coastal regions. Thus, it has become increasingly important to determine the 3D extent of these ocean features ("plumes") and how they evolve over time. The ocean environment is largely inaccessible to sensing directly by humans, motivating the need for robots to intelligently sense the ocean for us. In this paper, we propose the use of an autonomous underwater vehicle (AUV) network to track and predict plume shape and motion, discussing solutions to the challenges of spatiotemporal data aliasing (coverage versus resolution), underwater communication, AUV autonomy, data fusion, and coordination of multiple AUVs. A plume simulation is also developed here as the first step toward implementing behaviors for autonomous, adaptive plume tracking with AUVs, modeling a plume as a sum of Fourier orders and examining the resulting errors. This is then extended to include plume forecasting based on time variations, and future improvements and implementation are discussed.

Abstracte capabilities of autonomous underwater vehicles (AUVs) and their ability to perform tasks both autonomously and adaptively are rapidly improving, and the desire to quickly and efficiently sample the ocean environment as Earth's climate changes and natural disasters occur has increased significantly in the last decade. As such, this thesis proposes to develop a method for single and multiple AUVs to collaborate autonomously underwater while autonomously adapting their motion to changes in their local environments, allowing them to sample and track various features of interest with greater efficiency and synopticity than previously possible with preplanned AUV or ship-based surveys. is concept is demonstrated to work in field testing on multiple occasions: with a single AUV autonomously and adaptively tracking the depth range of a thermocline or acousticline, and with two AUVs coordinating their motion to collect a data set in which internal waves could be detected. is research is then taken to the next level by exploring the problem of adaptively and autonomously tracking spatiotemporally dynamic underwater fronts and plumes using individual and autonomously collaborating AUVs. Stephanie entered the Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Graduate Program following college to study Oceanographic Engineering. Her Doctoral research, which comprises this thesis, has focused on using autonomous underwater vehicles (AUVs) to perform autonomous and environmentally adaptive sampling of the ocean environment, focusing on underwater feature detection and tracking for more efficient and synoptic data collection with AUVs. Stephanie has enjoyed being on and near the ocean, working with oceanographic vehicles and instrumentation, and seeing her complex underwater vehicle missions work out in the water, and she has had the great opportunity of participating in over a dozen research cruises since she first started working with underwater vehicles in college. roughout graduate school, Stephanie has continued her many hobbies and activities, adding hiking, farming, sailing, and woodworking to the list. I would also like to thank all the members of the LAMSS lab past and present: ank you Toby for fixing nearly any software problem we threw at you on cruise and in the office, and for completing our A-team for every cruise we were on together. ank you also for being my partner in crime on cruise, on travel, and in life. I could not have completed this research nearly as efficiently without you there to devise all sorts of work-arounds when I wanted extra features to make my virtual experiments easier to run. ank you to Erin and Sheida for being both great friends and lab mates and for teaching me that it is okay to not be the only woman in an engineering lab! anks Stephanie for always being excited about my research and hosting craft nights. om, thanks for being such a quick learner on cruise and generally a fun person to hang out with. AcknowledgmentsYou will be a good rep...