The transportation infrastructure in the United States is a complex system that is vital to the everyday operations of the country. Bridges are a significant asset of this network, with many of them approaching the end of their service life. Corrosion is a common cause of deterioration which ultimately results to structural deficiency for the aging bridges. The deterioration rate is a multi-aspect factor that makes bridge inspections crucial. However, the current bridge inspections are very costly and potentially unsafe for the involved personnel. To lower costs and increase safety, many state DOT’s and universities have decided to perform research on Unmanned Aerial Vehicles (UAVs), or drones. This thesis explores the implementation of drone technology in bridge inspections and investigates their limits for corrosion detection and estimation. The first part of this thesis summarizes the responses obtained from a questionnaire sent to the personnel from the Massachusetts Department of Transportation (MassDOT). The second and third parts of this thesis summarizes how states have utilized UAVs for bridge inspections, including the selected drones and the attached equipment. The last part presents technologies that can be used to detect and measure corrosion, and how they can be used in conjunction with drones to quantify section loss of steel beams.

Unmanned Aircraft Systems (UAS) have grown in popularity due to their widespread potential applications, including efficient package delivery, monitoring, surveillance, search and rescue operations, agricultural uses, along with many others. As UAS become more integrated into our society and airspace, it is anticipated that the development and maintenance of a path planning collision-free system will become imperative, as the safety and efficiency of the airspace represents a priority. The dissertation defines this problem as the UAS Collision-free Path Planning Problem. The overall objective of the dissertation is to design an on-demand, efficient and scalable aerial highway path planning system for UAS. The dissertation explores two solutions to this problem. The first solution proposes a space-time algorithm that searches for shortest paths in a space-time graph. The solution maps the aerial traffic map to a space-time graph that is discretized on the inter-vehicle safety distance. This helps compute safe trajectories by design. The mechanism uses space-time edge pruning to maintain the dynamic availability of edges as vehicles move on a trajectory. Pruning edges is critical to protect active UAS from collisions and safety hazards. The dissertation compares the solution with another related work to evaluate improvements in delay, run time scalability, and admission success while observing up to 9000 flight requests in the network. The second solution to the path planning problem uses a batch planning algorithm. This is a new mechanism that processes a batch of flight requests with prioritization on the current slack time. This approach aims to improve the planning success ratio. The batch planning algorithm is compared with the space-time algorithm to ascertain improvements in admission ratio, delay ratio, and running time, in scenarios with up to 10000 flight requests. / Includes bibliography. / Dissertation (PhD)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Fixed-wing UAVs are today used in many different areas, from agriculture to search and rescue operations. Through various research efforts, they are becoming more and more autonomous. However, the procedure of landing a fixed-wing UAV remains a challenging task, which requires manual input from an experienced pilot. This work proposes a novel method which autonomously performs such landings. The main focus is on small and light-weight UAVs, for which the wind acts as a major disturbance and has to be taken into account. Robustness to other disturbances, such as variations in environmental factors or measurement errors, has also been prioritized during the development of this method.The main contribution of this work consists of a framework in which der\-iva\-tive-free optimization is used to calculate a set of waypoints, which are feasible to use in different wind speeds and directions, for a selected UAV model. These waypoints are then combined online using motion planning techniques, to create a trajectory which safely brings the UAV to a position where the landing descent can be initiated. To ensure a safe descent in a predefined area, another nonlinear optimization problem is formulated and solved. Finally, the proposed method is implemented on a real UAV platform. A number of simulations in different wind conditions are performed, and data from a real flight experiment is presented. The results indicate that the method successfully calculates feasible landing sequences in different scenarios, and that it is applicable in a real-world landing.

>Magister Scientiae - MSc / The fifth-generation mobile network (5G) is a new global wireless standard that enables state-of-the-art mobile networks with enhanced cellular broadband services that support a diversity of devices. Even with the current worldwide advanced state of broadband connectivity, most rural and low-income settings lack minimum Internet connectivity because there are no economic incentives from telecommunication providers to deploy wireless communication systems in these areas. Using a team of Unmanned Aerial Vehicles (UAVs) to extend or solely supply the 5G coverage is a great opportunity for these zones to benefit from the advantages promised by this new communication technology. However, the deployment and applications of innovative technology in rural locations need extensive research.

<p>  </p> <p>Motor vehicle crashes at represent a major cause of fatalities and injuries. As such, road transportation agencies continue to seek proactive intersection traffic monitoring initiatives to reduce crashes. Proper monitoring and assessment of crash risk can not only help identify and mitigate safety hazards in real time but also enhance the design of safer facilities and promote safe road-user behavior. It has been suggested in recent literature that unmanned aerial vehicles (UAVs), given their wide visual field and movement flexibility, can potentially help monitor road traffic operations when deployed on the field. In addition, it is recognized that it is still challenging to realize a large-scale ground-based vehicle-to-everything (V2X) network at the current time and in the very near future. In this regards, UAVs can play a critical connectivity role by serving as a hub to facilitate communications among roadway entities (vehicles, infrastructure, and pedestrians). This thesis first presented a methodology that integrates UAVs and V2X connectivity to track the trajectory of intersection users and to monitor potential collisions at intersections. The proposed methodology includes deep-learning-based tracking algorithms and time-to-collision assessments. The methodology was applied using a case study, and the results demonstrated the efficacy of the tracking methodology. Next, the thesis addressed the issue of image quality. During inclement weather, traffic monitoring can be challenging because the video quality is often corrupted by streaks of falling rain on the video image. This may hinder the reliability of characterizing the road environment and road-user behavior during such events. To fully exploit the benefits of video captured by UAVs in traffic monitoring, crash risk assessment, and other safety-related domains, it is critical to ensure high video quality. Therefore, this thesis proposed a two-stage self-supervised learning method to remove rain streaks in traffic videos, where the first and second stages address intra- and inter-frame noise, respectively. The results suggest that the proposed method provides satisfactory performance.</p> <p>  </p>

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

Surveillance is an important application for unmanned aerial vehicles (UAVs). The sensed information often has high priority and it must be made available to human operators as quickly as possible. Due to obstacles and limited communication range, it is not always possible to transmit the information directly to the base station. In this case, other UAVs can form a relay chain between the surveillance UAV and the base station. Determining suitable positions for such UAVs is a complex optimization problem in and of itself, and is made even more difficult by communication and surveillance constraints. To solve different variations of finding positions for UAVs for surveillance of one target, two new algorithms have been developed. One of the algorithms is developed especially for finding a set of relay chains offering different trade-offs between the number of UAVsand the quality of the chain. The other algorithm is tailored towards finding the highest quality chain possible, given a limited number of available UAVs. Finding the optimal positions for surveillance of several targets is more difficult. A study has been performed, in order to determine how the problems of interest can besolved. It turns out that very few of the existing algorithms can be used due to the characteristics of our specific problem. For this reason, an algorithm for quickly calculating positions for surveillance of multiple targets has been developed. This enables calculation of an initial chain that is immediately made available to the user, and the chain is then incrementally optimized according to the user’s desire.

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Deploying unmanned aerial vehicles (UAVs) as aerial base stations (BSs) in order to assist terrestrial connectivity, has drawn significant attention in recent years. UAV-BSs can take over quickly as service providers during natural disasters and many other emergency situations when ground BSs fail in an unanticipated manner. UAV-BSs can also provide cost-effective Internet connection to users who are out of infrastructure. UAV-BSs benefit from their mobility nature that enables them to change their 3D locations if the demand of ground users changes. In order to effectively make use of the mobility of UAV-BSs in a dynamic network and maximize the performance, the 3D location of UAV-BSs should be continuously optimized. However, solving the location optimization problem of UAV-BSs is NP-hard with no optimal solution in polynomial time for which near optimal solutions have to be exploited. Besides the conventional solutions, i.e. heuristic solutions, machine learning (ML), specifically reinforcement learning (RL), has emerged as a promising solution for tackling the positioning problem of UAV-BSs. The common practice for optimizing the 3D location of UAV-BSs using RL algorithms is assuming fixed location of ground users (i.e., UEs) in addition to defining discrete and limited action space for the agent of RL. In this thesis, we focus on improving the location optimization of UAV-BSs in two ways: 1-Taking into account the mobility of users in the design of RL algorithm, 2-Extending the action space of RL to a continuous action space so that the UAV-BS agent can flexibly change its location by any distance (limited by the maximum speed of UAV-BS). Three types of RL algorithms, i.e. Q-learning (QL), deep Q-learning (DQL) and actor-critic deep Q-learning (ACDQL) have been employed in this thesis to step-by-step improve the performance results of a UAV-assisted cellular network. QL is the first type of RL algorithm we use for the autonomous movement of the UAV-BS in the presence of mobile users. As a solution to the limitations of QL, we next propose a DQL-based strategy for the location optimization of the UAV-BS which largely improves the performance results of the network compared to the QL-based model. Third, we propose an ACDQL-based solution for autonomously moving the UAV-BS in a continuous action space wherein the performance results significantly outperforms both QL and DQL strategies.

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage.