DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Jason Moulton Advisor : Dr. Ioannis Rekleitis Date : May 3rd, 2019 Time : 9:00 am Place : Meeting Room 2267, Innovation Center Abstract This dissertation has four main contributions. The first contribution is the design and build of a fleet of long\hyp range, medium\hyp duration deployable autonomous surface vehicles (ASV). The second is the development, implementation and testing of inexpensive sensors to accurately measure wind, current, and depth environmental variables. 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\hyp 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 power 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 communities use. More selfishly, in order to enable our end\hyp state goal of deploying our ASV in adverse environments, we realize the requirement to measure the same environmental characteristics in real\hyp 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 experimental results in differing conditions, augmenting the trajectory tracking performance of the original way\hyp point navigation controller with our external forces feed\hyp forward algorithm.