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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.

Unmanned aerial vehicles (UAVs), commonly known as drones, have revolutionized numerous fields ranging from aerial photography to surveillance and logistics. Achieving stable flight is essential for their successful operation, ensuring accurate data acquisition, reliable manoeuvring, and safe operation. This thesis explores the feasibility of employing a frontal mono camera and sensor fusion techniques to enhance drone stability during flight. The objective of this research is to investigate whether a frontal mono camera, combined with sensor fusion algorithms, can be used to effectively stabilize a drone in various flight scenarios. By leveraging machine vision techniques and integrating data from onboard gyroscopes, the proposed approach aims to provide real-time feedback for controlling the drone. The methodology for this study involves the Crazyflie 2.1 drone platform equipped with a frontal camera and an Inertial Measurement Unit (IMU). The drone’s flight data, including position, orientation, and velocity, is continuously monitored and analyzed using Kalman Filter (KF). This algorithm processes the data from the camera and the IMU to estimate the drone’s state accurately. Based on these estimates, corrective commands are generated and sent to the drone’s control system to maintain stability. To evaluate the effectiveness of the proposed system, a series of flight tests are conducted under different environmental conditions and flight manoeuvres. Performance metrics such as drift, level of oscillations, and overall flight stability are analyzed and compared against baseline experiments with conventional stabilization methods. Additional simulated tests are carried out to study the effect of the communication delay. The expected outcomes of this research will contribute to the advancement of drone stability systems. If successful, the implementation of a frontal camera and sensor fusion can provide a cost-effective and lightweight solution for stabilizing drones.

<div>Unmanned aerial vehicles (UAVs) have gained more attention in recent years because of their ability to execute various missions. However, recent works have identified vulnerabilities in UAV systems that make them more readily prone to cyberattacks. In this work, the vulnerabilities in the communication channel between the UAV and ground control station are exploited to implement cyberattacks, specifically, the denial of service and false data injection attacks. Unlike other related studies that implemented attacks in simulations, we demonstrate the actual implementation of these attacks on a Holybro S500 quadrotor with PX4 autopilot firmware and MAVLink communication protocol.</div><div><br></div><div>The goal was to create a lightweight intrusion detection system (IDS) that leverages recurrent neural networks (RNNs) to accurately detect cyberattacks, even when implemented on a resource-constrained platform. Different types of RNNs, including simple RNNs, long short-term memory, gated recurrent units, and simple recurrent units, were trained and tested on actual experimental data. A recursive feature elimination approach was carried out on selected features to remove redundant features and to create a lighter RNN IDS model. We also studied the resource consumption of these RNNs on an Arduino Uno board, the lowest-cost companion computer that can be implemented with PX4 autopilot firmware and Pixhawk autopilot boards. The results show that a simple RNN has the best accuracy while also satisfying the constraints of the selected computer.<br></div>

Monitoring the structural health of civil infrastructures with wireless sensor networks aids in detecting failures early, but faces power challenges in ensuring reasonable network lifetimes. Recharging select nodes with Unmanned Aerial Vehicles (UAVs) provides a solution that currently can recharge a single node; however, questions arise on the effectiveness of a limited recharging system, the appropriate node to recharge, and the best sink selection algorithm for improving network lifetime given a limited recharging system. This paper simulates such a network in order to answer those questions. This thesis first determines whether or not recharging with a UAV is an effective method of delivering limited power to the network. It then determines the best way to deliver that power. Finally, this thesis explores five different sink positioning algorithms to find which optimize the network lifetime by load-balancing the energy in the network, all in combination with the added capability of a UAV.

Indoor flight of unmanned aerial vehicles (UAVs) has many applications in environments in which it is undesirable or dangerous for humans to be, such as military reconnaissance or searching for trapped victims in a collapsed building. However, limited visual feedback makes it difficult to pilot UAVs in cluttered and enclosed spaces. Haptic feedback combined with visual feedback has shown to reduce the number of collisions of UAVs in indoor environments; however, it has increased the mental workload of the operator. This thesis investigates the potential of combining novel haptic and 3D audio feedback to provide additional information to operators of UAVs in order to improve performance and reduce workload. Many haptic feedback algorithms, such as Time to Impact (TTI)~cite{Brandt2009}, have been developed to help pilot UAVs. This thesis compares TTI with two new haptic feedback algorithms: Omni-Directional Dynamics Springs (ODDS) and Velocity Scaled Omni-Directional Dynamic Springs (VSODDS). These novel algorithms are based on the idea that dynamic springs are attached to the haptic controller in all directions. This thesis is unique by augmenting visual and haptic feedback with real-time 3D audio feedback. Continuous Directional Graded Threshold (CDGT) and Discrete Directional Graded Threshold (DDGT) are two novel algorithms that were developed to provide 3D audio warning cues to operators. To reduce sensory overload, these algorithms play a graded audio alert cue in the direction of velocity and when within a threshold distance of an obstacle. In order to measure operator workload, many researchers have used subjective measures, which suffer from subject bias, preconceptions, and ordering. Instead of using a subjective measure, experimental data is used to objectively measure operator workload using behavioral entropy, which works on the idea that humans work to reduce entropy by skilled behavior. QuadSim, a robust and versatile indoor quadrotor simulator, was developed as a test bed for visual, haptic, and 3D audio feedback. Using QuadSim, a human subject experiment was performed to determine the effectiveness of haptic and 3D audio feedback on operator performance and workload. The results of the study indicate that haptic feedback significantly reduced the number of collisions and collision length. Operator workload was decreased in the side-to-side direction by VSODDS but was adversely increased by TTI. Overall, VSODDS outperformed the other haptic algorithms. Unlike haptic feedback, audio feedback proved to be neither helpful nor harmful in improving performance or reducing workload.

This thesis proposes the design of the UAV-LMDS communication system for military and commercial use. The UAV-LMDS system is a digital, wireless communication system that provides service using unmanned aerial vehicles (UAVs) flying at 60,000 ft. acting as communication hubs. This thesis provides background information on UAV-LMDS system elements, a financial analysis, theory, link budgets, system component design and implementation issues. To begin the design, we develop link budgets that are used to characterize system parameters. We present detailed antenna designs for the antennas aboard the UAV. We also present communication equipment block diagrams. Included are technical details on military and commercial geostationary satellites used to link transmissions in the system. Implementation issues in the military system are discussed. Mobility and the effects of vegetation in the propagation path are investigated and a co-channel interference study is done. This thesis shows that by using UAVs and LMDS, a viable, broadband, wireless communications system can be created for military and commercial use. / Master of Science

Yes / The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations. / Supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries(IPET) through Agri-Bio Industry Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA) (316033-04-2-338 SB030).

The industry of the civil Unmanned Aerial Vehicles (UAVs) has been growing rapidly in past few years. In many scenarios, accomplishing a task using a single UAV is either not cost-effective due to the size of the project or not even feasible due to the existence of unforeseen environment conditions and constraints (e.g., weather conditions and/or physical obstacles). This limitation motivates the need to move to solutions that incorporate a network of autonomous UAVs that carry out a joint and coordinated mission. This thesis introduces a multi-agent system and related algorithms that solve the graph traversal problem in a distributed and decentralized manner while optimizing a set of costs. The environment is modelled as a graph where every node is the point for the agents to accomplish some task or to distinguish the point as an obstacle where traveling is not possible. The online distributed algorithms are implemented on a network of UAVs and we report the results of rigorous simulations and real experiments with a network of UAVs. The results clearly validate our claim that a network UAVs can be effectively employed to accomplish a given task. / Thesis / Master of Science (MSc)