Under ever intensifying pressures from land use, climate change, and erosion, tropical islands are among the most vulnerable systems in the world. Terrestrial systems are weakened by intensifying land use patterns, the weakening of which is highlighted when high intensity rainfall events erode sediment and leads to sediment deposition on the marine system. The deposition of sediment on the marine system is a major stressor that can lead to weakened coral reefs and a decrease in marine resources commonly gathered for food. These interactions have led to the emergence of biocultural resource management strategies, one of which is the ahupua'a system. The ahupua'a system, at some scales, is an example of a resilient resource management strategy that has held up despite the pressures and challenges of living on a tropical island. Here we utilize a combination of unmanned aerial vehicles (UAVs or drones) and autonomous surface vehicles (ASV) to gather imagery that is then used in geospatial analyses to better understand the ahupua'a of Ka'amola as well as evaluate coral reef structure along the south shore of Molokai. Our terrestrial work using UAVs and geospatial analyses supports qualitative data from community members and local land managers regarding sediment movement trends they have noticed. Steep slopes coupled with a weakened landscape and decreasing vegetative cover due to ungulate grazing has primed the area for erosion during high intensity rainfall events. Our marine work matches trends observed in previous studies and highlights the value in utilizing an ASV to perform marine remote sensing while also acknowledging the limitations associated with a system such as the one built for our research work.

As the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles. / Master of Science

<p>This thesis presents solutions to some of the problems facing Autonomous Surface Vehicle (ASV) deployments in inland waterways through the development of navigational and control systems. Fluvial systems are one of the hardest inland waterways to navigate and are thus used as a use-case for system development. The systems are built to reduce the reliance on a-prioris during ASV operation. This is crucial for exceptionally dynamic environments such as fluvial bodies of water that have poorly defined routes and edges, can change course in short time spans, carry away and deposit obstacles, and expose or cover shoals and man-made structures as their water level changes. While navigation of fluvial systems is exceptionally difficult potential autonomous data collection can aid in important scientific missions in under studied environments.</p> <p><br></p> <p>The work has four contributions targeting solutions to four fundamental problems present in fluvial system navigation and control. To sense the course of fluvial systems for navigable path determination a fluvial segmentation study is done and a novel dataset detailed. To enable rapid path computations and augmentations in a fast moving environment a Dubins path generator and augmentation algorithm is presented ans is used in conjunction with an Integral Line-Of-Sight (ILOS) path following method. To rapidly avoid unseen/undetected obstacles present in fluvial environments a Deep Reinforcement Learning (DRL) agent is built and tested across domains to create dynamic local paths that can be rapidly affixed to for collision avoidance. Finally, a custom low-cost and deployable ASV, BREAM (Boat for Robotic Engineering and Applied Machine-Learning), capable of operating in fluvial environments is presented along with an autonomy package used in providing base level sensing and autonomy processing capability to varying platforms.</p> <p><br></p> <p>Each of these contributions form a part of a larger documented Fluvial Navigation Control Architecture (FNCA) that is proposed as a way to aid in a-priori free navigation of fluvial waterways. The architecture relates the navigational structures into high, mid, and low-level controller Guidance and Navigational Control (GNC) layers that are designed to increase cross vehicle and domain deployments. Each component of the architecture is documented, tested, and its application to the control architecture as a whole is reported.</p>