Author

Jason Moulton

Date of Award

Fall 2019

Document Type

Open Access Dissertation

Department

Computer Science and Engineering

First Advisor

Ioannis Rekleitis

Abstract

This dissertation has four main contributions. The first contribution is the design and build of a fleet of long-range, medium-duration deployable autonomous surface vehicles (ASV). The second is the development, implementation, and testing of inex-pensive sensors to accurately measure wind, current, and depth environmental vari- ables. The third leverages the first two contributions, and is modeling the effects of environmental variables on an ASV, finally leading to the development of a dynamic controller enabling deployment in more uncertain conditions.

The motivation for designing and building a new ASV comes from the lack of availability of a flexible and modular platform capable of long-range deployment in current state of the art. We present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient batteries to power components and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design, lessons learned during build and deployments, as well as a comprehensive build tutorial is provided in this thesis.

The contributions from developing an inexpensive environmental sensor suite are multi-faceted. The ability to monitor, collect, and build models of depth, wind, and current in environmental applications proves to be valuable and challenging, where we illustrate our capability to provide an efficient, accurate, and inexpensive data collection platform for the community’s use. More selfishly, in order to enable our end- state goal of deploying our ASV in adverse environments, we realize the requirement to measure the same environmental characteristics in real-time and provide them as inputs to our effects model and dynamic controller. We present our methods for calibrating the sensors and the experimental results of measurement maps and prediction maps from a total of 70 field trials.

Finally, we seek to inculcate our measured environmental variables along with previously available odometry information to increase the viability of the ASV to maneuver in highly dynamic wind and current environments. We present experimen- tal results in differing conditions, augmenting the trajectory tracking performance of the original way-point navigation controller with our external forces feed-forward algorithm.

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