The tremendous success of various wireless applications operating in unlicensed bands has resulted in the overcrowding of those bands. Cognitive radio (CR) is a new technology that enables an unlicensed user to coexist with incumbent users in licensed spectrum bands without inducing interference to incumbent communications. This technology can significantly alleviate the spectrum shortage problem and improve the efficiency of spectrum utilization. Networks consisting of CR nodes (i.e., CR networks)---often called dynamic spectrum access networks or NeXt Generation (XG) communication networks---are envisioned to provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. In recent years, the operational aspects of CR networks have attracted great research interest. However, research on the security aspects of CR networks has been very limited. In this thesis, we discuss security issues that pose a serious threat to CR networks. Specifically, we focus on three potential attacks that can be launched at the physical or MAC layer of a CR network: primary user emulation (PUE) attack, spectrum sensing data falsification (SSDF) attack, and control channel jamming (CCJ) attack. These attacks can wreak havoc to the normal operation of CR networks. After identifying and analyzing the attacks, we discuss countermeasures. For PUE attacks, we propose a transmitter verification scheme for attack detection. The scheme utilizes the location information of transmitters together with their signal characteristics to verify licensed users and detect PUE attackers. For both SSDF attacks and CCJ attacks, we seek countermeasures for attack mitigation. In particular, we propose Weighted Sequential Probability Ratio Test (WSPRT) as a data fusion technique that is robust against SSDF attacks, and introduce a multiple-rendezvous cognitive MAC (MRCMAC) protocol that is robust against CCJ attacks. Using security analysis and extensive numerical results, we show that the proposed schemes can effectively counter the aforementioned attacks in CR networks. / Ph. D.

This paper investigates the feasibility of a rendezvous with an uncooperative space object using only optical sensors and takes a closer look at the performance of different algorithms used to estimate an object’s orbit. The ability to perform a rendezvous with an uncooperative target is critical for a wide variety of future missions, such as space debris removal. The main satellite, referred to as chaser, has to determine precisely the orbit of the space object of interest, referred to as target. After some elements of mission analysis, this report dives into the angles-only method of Initial Orbit Determination developed by Gooding, which is a method well suited for space-based observations. It gives access to the osculating orbit at the time of measurements. Then, the estimated orbit is refined using a Batch Least Squares algorithm. The accuracy of the orbit determination depends on the number and precision of the measurements. An optimal strategy for the distribution of the measurements on orbit is to take measurements regularly throughout the whole orbit. The constraints of eclipses and ground stations contacts are taken into account. Finally, the Rendezvous and Proximity Operations are explored in a mission scenario. / Detta examensarbete undersöker möjligheten av ett rendezvous med ett icke-samarbetsvilligt rymdobjekt som endast använder optiska sensorer och tar en närmare titt på prestandan hos olika algoritmer som används för att uppskatta ett objekts omloppsbana. Förmågan att utföra ett möte med ett icke samarbetsvilligt mål är avgörande för en mängd olika framtida uppdrag, till exempel borttagning av rymdskrot. Den viktigaste satelliten, kallad chaser, måste exakt bestämma omloppsbanan för rymdföremål av intresse, kallad target. Efter några element av uppdragsanalys, dyker denna rapport in i den vinkelbaserade metoden för initial omloppsbestämning som utvecklats av Gooding, som är en metod som är väl lämpad för rymdbaserade observationer. Den ger tillgång till den osulerande banan vid tidpunkten för mätningarna. Därefter förfinas den beräknade omloppsbanan med hjälp av minstakvadratmetoden. Noggrannheten i omloppsbestämningen beror på antalet mätningar och deras precision. En optimal strategi för fördelningen av mätningarna i omloppsbana är att göra mätningar regelbundet över hela omloppsbanan. Begränsningarna i förmörkelser och markstationernas kontakter beaktas. Slutligen utforskas mötes- och närhetsoperationer i ett uppdragsscenario.

This thesis addresses the problem of positional consensus of multi-agent systems. A positional consensus is achieved when the agents converge to a point. Some applications of this class of problem is in mid-air refueling of the aircraft or UAVs, targeting a geographical location, etc. In this research work some positional consensus algorithms have been developed. They can be categorized in two part (i) Broadcast control based algorithm (ii) Distributed control based algorithm. In case of broadcast based algorithm control strategies for a group of agents is developed to achieve positional consensus. The problem is constrained by the requirement that every agent must be given the same control input through a broadcast communication mechanism. Although the control command is computed using state information in a global framework, the control input is implemented by the agents in a local coordinate frame. The mathematical formulation has been done in a linear programming framework that is computationally less intensive than earlier proposed methods. Moreover, a random perturbation input in the control command, that helps to achieve reasonable proximity among agents even for a large number of agents, which was not possible with the existing strategy in the literature, is introduced. This method is extended to achieve positional consensus at a pre-specified location. A comparison between the LP approach and the existing SOCP based approach is also presented. Some of the algorithm has been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots. In the second case of broadcast based algorithm, a decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus has been developed using the broadcast concept. Even here, the mathematical formulation has done using a linear programming framework. Each agent has some sensing radius and it is capable of sensing position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. In case of distributed algorithms, a computationally efficient distributed rendezvous algorithm for a group of autonomous agents has been developed. The algorithm uses a rectilinear decision domain (RDD), as against the circular decision domain assumed in earlier work available in the literature. This helps in reducing its computational complexity considerably. An extensive mathematical analysis has been carried out to prove the convergence of the algorithm. The algorithm has also been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots.

On-orbit servicing (OOS) of space systems provides immense benefits by extending their lifetime, by reducing overall cost of space operations, and by adding flexibility to space missions. Refueling is an important aspect of OOS operations. The problem of determining the optimal strategy of refueling multiple satellites in a constellation, by expending minimum fuel during the orbital transfers, is challenging, and requires the solution of a large-scale optimization problem. The conventional notion about a refueling mission is to have a service vehicle visit all fuel-deficient satellites one by one and deliver fuel to them. A recently emerged concept, known as the peer-to-peer (P2P) strategy, is a distributed method of replenishing satellites with fuel. P2P strategy is an integral part of a mixed refueling strategy, in which a service vehicle delivers fuel to part (perhaps half) of the satellites in the constellation, and these satellites, in turn, engage in P2P maneuvers with the remaining satellites. During a P2P maneuver between a fuel-sufficient and a fuel-deficient satellite, one of them performs an orbital transfer to rendezvous with the other, exchanges fuel, and then returns back to its original orbital position. In terms of fuel expended during the refueling process, the mixed strategy outperforms the single service vehicle strategy, particularly with increasing number of satellites in the constellation. This dissertation looks at the problem of P2P refueling problem and proposes new extensions like the Cooperative P2P and Egalitarian P2P strategies. It presents an overview of the methodologies developed to determine the optimal set of orbital transfers required for cooperative and non-cooperative P2P refueling strategies. Results demonstrate that the proposed strategies help in reducing fuel expenditure during the refueling process.

This thesis addresses several aerospace guidance and decision making problems using both no cooperative and cooperative game theoretical solution concepts in the differential games framework. In the first part of the thesis, state dependent Riccati equation (SDRE) method has been extended to a zero-sum nonlinear differential games setting. This framework is used to study problems of intercepting a manoeuvring target, with and without terminal impact angle constraints, in the zero-sum differential games theory perspective. The guidance laws derived according to the proposed method are in closed from and online implementable. In the second part of the thesis, cooperative game theoretic concepts are applied to make a group of unmanned aerial vehicles (UAV) achieve rendezvous, in a given finite time horizon. An algorithm has been proposed that enables the UAVs to realize Nash bargaining solution. In this context, criteria for time consistency of a cooperative solution of nonzero-sum linear quadratic differential games have been studied. The problems where the UAVs try to achieve rendezvous by implementing cooperative game theoretic strategies, based on local information structure only, is also addressed.

The aim of this thesis is to explore the elements of power-knowledge in two SF novels written amid the Space Race during the Cold War era. While the dominant interest of both Stanislaw Lem’s Solaris and Arthur C. Clarke’s Rendezvous with Rama generally revolves around the implications of human interactions with an alien presence, my focus is primarily on the power structures that propel those interactions: questioning the intentions of scientific pursuits and analyzing the effects of Foucauldian power relations on the human individual. I do this by applying Foucault’s theories of the duality of the subject and his work on biopolitics. What is gleaned is not only a study of the interests of power, but an emphasis on the intersectional restrictions of power and cognition. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Cette thèse porte sur la navigation d'agents dans un environnement manufacturier. Le cadre général du travail relève de la navigation d'AGVs (véhicules autoguidés), transportant librement et intelligemment leur produit. L'objectif est de proposer des outils permettant la navigation autonome et coopérative d’une flotte d’AGVs dans des systèmes de production manufacturiers où les contraintes temporelles sont importantes. Après la présentation d'un état de l'art sur chaque domaine (systèmes manufacturiers et navigation d'agents), les impacts de la mutualisation entre ceux-ci sont présentés. Ensuite, deux problématiques, liées à la navigation d'agents mobiles dans des environnements manufacturiers, sont étudiées. La première problématique est centrée sur la planification de trajectoire décentralisée où une fonction d'ordonnancement est combinée au planificateur pour chaque agent. Cette fonction permet de choisir une ressource lors de la navigation afin d'achever l'opération du produit transporté le plus tôt possible. La première solution consiste en une architecture hétérarchique où les AGVs doivent planifier (ou mettre à jour) leur trajectoire, ordonnancer leur produit pour l'opération en cours et résoudre leurs propres conflits avec les agents à portée de communication. Pour la seconde approche, une architecture hybride à l'aide d'un superviseur, permettant d'assister les agents durant leur navigation, est proposée. L'algorithme de planification de trajectoire se fait en deux étapes. La première étape utilise des informations globales fournies par le superviseur pour anticiper les collisions. La seconde étape, plus locale, utilise les données par rapport aux AGVs à portée de communication afin d'assurer l'évitement de collisions. Afin de réduire les temps de calcul des trajectoires, une optimisation par essaims particulaires est introduite. La seconde problématique se focalise sur la commande coopérative permettant un rendez-vous d'agents non holonomes à une configuration spécifique. Ce rendez-vous doit être atteint en un temps donné par un cahier des charges, fourni par le haut-niveau de contrôle. Pour résoudre ce problème de rendez-vous, nous proposons une loi de commande à temps fixe (i.e. indépendant des conditions initiales) par commutation permettant de faire converger l’état des AGVs vers une resource. Des résultats numériques et expérimentaux sont fournis afin de montrer la faisabilité des solutions proposées. / This thesis is focused on agent navigation in a manufacturing environment. The proposed framework deals with the navigation of AGVs (Automated Guided Vehicles), which freely and smartly transport their product. The objective is to propose some tools allowing the autonomous and cooperative navigation of AGV fleets in manufacturing systems for which temporal constraints are important. After presenting the state of the art of each field (manufacturing systems and agent navigation), the impacts of the cross-fertilization between these two fields are presented. Then, two issues, related to the navigation of mobile agents in manufacturing systems, are studied. The first issue focuses on decentralized motion planning where a scheduling function is combined with the planner for each agent. This function allows choosing a resource during the navigation to complete the ongoing operation of the transported product at the soonest date. The first proposed approach consists in a heterarchical architecture where the AGVs have to plan (or update) their trajectory, schedule their product and solve their own conflict with communicating agents. For the second approach, hybrid architecture with a supervisor, which assists agents during the navigation, is proposed. The motion planning scheme is divided into two steps. The first step uses global information provided by the supervisor to anticipate the future collisions. The second step is local and uses information from communicating agents to ensure the collision avoidance. In order to reduce the computational times, a particle swarm optimization is introduced. The second issue is focused on the cooperative control, allowing a rendezvous of nonholomic agents at a specific configuration. This rendezvous must be achieved in a prescribed time, provided by the higher level of control. To solve this rendezvous, a fixed time (i.e. independent of initial conditions) switching control law is proposed, allowing the convergence of agent states towards a resource configuration. Some numerical and experimental results are provided to show the feasibility of the proposed methods.

<div>Multi-robot systems have demonstrated strong capability in handling environmental operations. In this study, We examine how a team of robots can be utilized in covering and removing spill patches in a dynamic environment by executing three consecutive stages: deployment, coverage, and rendezvous. </div><div> </div><div>For the deployment problem, we aim for robot allocation based on the discreteness of the patches that need to be covered. With the deep neural network (DNN) based spill detector and remote sensing facilities such as drones with vision sensors and satellites, we are able to obtain the spill distribution in the workspace. Then, we formulate the allocation problem in a general optimization form and provide solutions using an integer linear programming (ILP) solver under several realistic constraints. After the allocation process is completed and the robot team is divided according to the number of spills, we deploy robots to their computed optimal goal positions. In the robot deployment part, control laws based on artificial potential field (APF) method are proposed and practiced on robots with a common unicycle model. </div><div> </div><div>For the coverage control problem, we show two strategies that are tailored for a wirelessly networked robot team. We propose strategies for coverage with and without path planning, depending on the availability of global information. Specifically, in terms of coverage with path planning, we partition the workspace from the aerial image into pieces and let each robot take care of one of the pieces. However, path-planning-based coverage relies on GPS signals or other external positioning systems, which are not applicable for indoor or GPS-denied circumstances. Therefore, we propose an asymptotic boundary shrink control that enables a collective coverage operation with the robot team. Such a strategy does not require a planned path, and because of its distributedness, it shows many advantages, including system scalability, dynamic spill adaptability, and collision avoidance. In case of a large-scale patch that poses challenges to robot connectivity maintenance during the operation, we propose a pivot-robot coverage strategy by mean of an a priori geometric tessellation (GT). In the pivot-robot-based coverage strategy, a team of robots is sent to perform complete coverage to every packing area of GT in sequence. Ultimately, the entire spill in the workspace can be covered and removed.</div><div> </div><div>For the rendezvous problem, we investigate the use of graph theory and propose control strategies based on network topology to motivate robots to meet at a designated or the optimal location. The rendezvous control strategies show a strong robustness to some common failures, such as mobility failure and communication failure. To expedite the rendezvous process and enable herding control in a distributed way, we propose a multi-robot multi-point rendezvous control strategy. </div><div> </div><div>To verify the validity of the proposed strategies, we carry out simulations in the Robotarium MATLAB platform, which is an open source swarm robotics experiment testbed, and conduct real experiments involving multiple mobile robots.</div>

This dissertation develops the visual pursuit method for air-to-air tracking and rendezvous of unmanned aircraft systems. It also shows the development of vector-field and proportional-integral methods for controlling UAS flight in formation with other aircraft. The visual pursuit method is a nonlinear guidance method that uses vision-based line of sight angles as inputs to the algorithm that produces pitch rate, bank angle and airspeed commands for the autopilot to use in aircraft control. The method is shown to be convergent about the center of the camera image frame and to be stable in the sense of Lyapunov. In the lateral direction, the guidance method is optimized to balance the pursuit heading with respect to the prevailing wind and the location of the target on the image plane to improve tracking performance in high winds and reduce bank angle effort. In both simulation and flight experimentation, visual pursuit is shown to be effective in providing flight guidance in strong winds. Visual pursuit is also shown to be effective in guiding the seeker while performing aerial docking with a towed aerial drogue. Flight trials demonstrated the ability to guide to within a few meters of the drogue. Further research developed a method to improve docking performance by artificially increasing the length of the line of sight vector at close range to the target to prevent flight control saturation. This improvement to visual pursuit was shown to be an effective method for providing guidance during aerial docking simulations. An analysis of the visual pursuit method is provided using the method of adjoints to evaluate the effects of airspeed, closing velocity, system time constant, sensor delay and target motion on docking performance. A method for predicting docking accuracy is developed and shown to be useful for predicting docking performance for small and large unmanned aircraft systems.

Computing with mobile agents is rapidly becoming a topic of mainstream research in the theory of distributed computing. The main research questions undertaken in this study concern the feasibility of solving fundamental tasks in an anonymous network, subject to limitations on the resources available to the agent. The considered challenges include: exploring a graph by means of an agent with limited memory, discovery of the network topology, and attempting to meet with another agent in another network (rendezvous). The constraints imposed on the agent include the number of moves which the agent is allowed to perform in the network, the amount of state memory available to the agent, the ability of the agent to communicate with other agents, as well as its a priori knowledge of the network topology or of global parameters.