Unmanned Aerial Vehicles for Communication and Civil Applications

Abstract

This dissertation focuses on using unmanned aerial vehicles (UAVs) for communication and civil applications. Nowadays, UAVs have found numerous applications across a proliferation of fields, such as aerial inspection, photography, precision agriculture, traffic control, search and rescue, package delivery, and telecommunications. While most related works use UAVs for single-task operations, an interesting idea is to design UAVs capable of simultaneously performing multiple tasks. This approach could significantly improve system efficiency. For example, consider a residential area where drones are used as last-mile delivery tools. However, it is unclear whether such mobility patterns for package delivery can provide uniform coverage within the area of interest. In this dissertation, we aim to design UAV trajectories that efficiently perform transportation operations (e.g., package delivery) while simultaneously providing uniform coverage over a neighborhood area. Such coverage is essential for applications like network coverage, Internet of Things (IoT) data collection, wireless power transfer, and surveillance. We first consider multi-task UAVs in a simplified scenario where the neighborhood area is a circular region. In this case, UAV missions start from the center, with destinations assumed to be uniformly distributed along the circle's boundary. We propose a trajectory planning process that achieves uniform coverage while preserving delivery efficiency. Subsequently, we explore a more practical scenario where transport destinations are arbitrarily distributed in an irregularly shaped region. We demonstrate that simultaneous uniform coverage and efficient transport trajectories (e.g., package delivery) are achievable in such realistic settings. This is substantiated through rigorous analysis and simulations.\\ Additionally, this dissertation examines the use of UAVs in disaster management. UAVs offer significant advantages in disaster response, search and rescue operations (SAR), and wildfire detection. For instance, in critical scenarios such as wildfires, avalanches, or searches for missing persons, UAVs can expedite disaster management and SAR efforts. Search efficiency is crucial in such operations because the likelihood of survival diminishes over time, and wildfire containment becomes increasingly challenging. In this work, we aim to optimize flight paths that maximize the probability of locating missing persons or detecting wildfires. The path optimization algorithm ensures full coverage of the area of interest by leveraging prior knowledge of target distribution. In the case of finding missing persons, this knowledge may be derived from behavioral analyses, while for wildfire detection, it can be obtained through expert assessment of terrain and emergency factors. The UAV follows the optimized flight path, collecting data for processing. We employ machine learning techniques to quickly and accurately identify targeted objects or detect fires. Specifically, we use residual neural networks (ResNet) to detect fire and smoke in wildland areas and to locate missing persons in snow-covered regions.Doctor of Philosophy (Ph.D.