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This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV) for the task of covering an environment. Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying on a fixed altitude plane parallel to the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to a coverage problem that covers 2D regions on the ground with a UAV that can operate in fixed-wing or multirotor mode. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions, not all of which lie on the same plane. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting planar regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in complete 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters viewpoints on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path.

The modern industrial society is built upon the productivity of advanced computing and agile machines assisting the human workforce to perform their tasks more effectively, accurately and efficiently. The industrial revolution termed as "Industry 4.0" is based on the intelligence of machines working with humans in a collaborative work-space. Contrarily, infrastructure management has relied on the human for making day to day decisions. New emerging technologies can assist during infrastructure inspections, to quantify structural condition with more objective data. However, today's’ owners agree in trusting the inspector's decision in the field over data collected with sensors. If data collected in the field would be accessible during the inspections, the inspector decisions would be improved with sensors. New research opportunities in the human-infrastructure interface would allow researchers to improve the human awareness of their surrounding environment during inspections. This MS thesis studies the role of Augmented Reality (AR) and sensor technology as tools to increase human awareness of infrastructure. The domains of interest of this research include both inspections and emergency scenarios where humans need fast information about their environment to save lives. The results of vi this MS thesis research are the design, programming, and validation of two applications using AR headsets: AR-QR code scanning and AR-sensor connection. The two new programs facilitate the interface of humans with infrastructure, laying out a new scenario for technology enabling new research, tools, and test-beds for human-infrastructure interface research. The results of this MS thesis lay the foundation for future growth in the area of machine-assisted human-centered structure inspections, emergency management and human-machine collaboration using AR.