We study the problem of viewpoint planning in static and dynamic scenes using multi-robot teams. This work is motivated by two applications: bridge inspection and environmental monitoring using Unmanned Aerial Vehicles. For static scenes, we are given a set of target points in a polygonal environment that must be monitored using robots with cameras. The goal is to compute a tour for all the robots such that every target is visible from at least one tour. We solve this problem optimally by reducing it to Generalized Travelling Salesman Problem. For dynamic scenes, we study the multi-robot assignment problem for multi-target tracking. The problem can be viewed as the mixed packing and covering problem. We optimally solve the problem using Mixed Quadratic Integer Linear Program to maximize the total number of targets covered. In addition to theoretical contribution, we also present our hardware system design and findings from field experiments. / Master of Science / We study the problem of viewpoint planning in static and dynamic scenes using multi-robot teams. This work is motivated by two applications: bridge inspection and environmental monitoring using Unmanned Aerial Vehicles. For static scenes, we are given a set of target points in a static 2D or 3D environment such as a bridge. Target points are key locations that we are interested to monitor using cameras on the robots. The goal is to compute a tour for all the robots such that every target location is visible from at least one robot’s tour. We want to minimize the sum of lengths of all the robot’s tours combined. We find the best possible solution for this problem. For dynamic scenes, we study the multi-robot trajectory assignment problem for multi-target tracking. Here, the target points may be moving, e.g., expanding plumes in an oil spill. The goal in this is to maximize the total number of targets covered at each time step. We provide the best possible solution in this case. In addition to theoretical contribution, we also present our hardware system design and findings from field experiments.

Society's increased use of network applications, such as email, social networking, and web browsing, creates a massive amount of information floating around in cyber space. An attacker can collect this information to build a profile of where people go, what their interests are, and even what they are saying to each other. For certain government and corporate entities, the exposure of this information could risk national security or loss of capital. This work identifies vulnerabilities in the way the Internet Protocol version 6 (IPv6) forms addresses. These vulnerabilities provide attackers with the ability to track a node's physical location, correlate network traffic with specific users, and even launch attacks against users' systems. A Moving Target IPv6 Defense (MT6D) that rotates through dynamically obscured network addresses while maintaining existing connections was developed to prevent these addressing vulnerabilities.MT6D is resistant to the IPv6 addressing vulnerabilities since addresses are not tied to host identities and continuously change. MT6D leverages the immense address space of IPv6 to provide an environment that is infeasible to search efficiently. Address obscuration in MT6D occurs throughout ongoing sessions to provide continued anonymity, confidentiality, and security to communicating hosts. Rotating addresses mid-session prevents an attacker from determining that the same two hosts are communicating. The dynamic addresses also force an attacker to repeatedly reacquire the target node before he or she can launch a successful attack. A proof of concept was developed that demonstrates the feasibility of MT6D and its ability to seamlessly bind new IPv6 addresses. Also demonstrated is MT6D's ability to rotate addresses mid-session without dropping or renegotiating sessions.This work makes three contributions to the state-of-the-art IPv6 research. First, it fully explores the security vulnerabilities associated with IPv6 address formation and demonstrates them on a production IPv6 network. Second, it provides a method for dynamically rotating network addresses that defeats these vulnerabilities. Finally, a functioning prototype is presented that proves how network addresses can be dynamically rotated without losing established network connections. If IPv6 is to be globally deployed, it must not provide additional attack vectors that expose user information. / Ph. D.

The amount of data that is shared on the Internet is growing at an alarming rate. Current estimates state that approximately 2.5 exabytes of data were generated every day in 2012. This rate is only growing as people continue to increase their on-line presence. As the amount of data grows, so too do the number of people who are attempting to gain access to the data. Attackers try many methods to gain access to information, including a number of attacks that occur at the network layer. A network-based moving target defense is a technique that obfuscates the location of a machine on the Internet by arbitrarily changing its IP address periodically. MT6D is one of these techniques that leverages the size of the IPv6 address space to make it statistically impossible for an attacker to find a specific target machine. MT6D was designed with a number of limitations that include manually generated static configurations and support for only peer to peer networks. This work presents extensions to MT6D that provide dynamically generated configurations, a secure and dynamic means of exchanging configurations, and with these new features, an ability to function as a server supporting a large number of clients. This work makes three primary contributions to the field of network-based moving target defense systems. First, it provides a means to exchange arbitrary information in a way that provides network anonymity, authentication, and security. Second, it demonstrates a technique that gives MT6D the capability to exchange configuration information by only sharing public keys. Finally, it introduces a session establishment protocol that clients can use to establish concurrent connections with an MT6D server. / Ph. D.

The use of autonomous aerial vehicles (UAVs) to explore unknown environments is a growing field of research; of particular interest is locating a target that emits a signal within an unknown environment. Several physical processes produce scalar signals that attenuate with distance from their source, such as chemical, biological, electromagnetic, thermal, and radar signals. The natural decay of the signal with increasing distance enables a gradient ascent method to be used to navigate toward the target. The UAVs navigate around obstacles whose positions are initially unknown; a hybrid controller comprised of overlapping control modes enables robust obstacle avoidance in the presence of exogenous inputs by precluding topological obstructions. Limitations of a distributed gradient augmentation approach to obstacle avoidance are discussed, and an alternative algorithm is presented which retains the robustness of the hybrid control while leveraging local obstacle position information to improve non-collision reliability. A heterogeneous swarm of multirotors demonstrates the target locating problem, sharing information over a multicast wireless private network in a fully distributed manner to form an estimate of the signal's gradient, informing the direction of travel toward the target. The UAVs navigate around obstacles, showcasing both algorithms developed for obstacle avoidance. Each UAV performs its own target seeking and obstacle avoidance calculations in a distributed architecture, receiving position data from an OptiTrack motion capture system, illustrating the applicability of the control law to real world challenges (e.g., unsynchronized clocks among different UAVs, limited computational power, and communication latency). Experimental and theoretical results are compared. / Master of Science / In this project, a new method for locating a target using a swarm of unmanned drones in an unknown environment is developed and demonstrated. The drones measure a signal such as a beacon that is being emitted by the target of interest, sharing their measurement information with the other drones in the swarm. The magnitude of the signal increases as the drones move toward the target, allowing the drones to estimate the direction to the target by comparing their measurements with the measurements collected by other drones. While seeking the target in this manner, the drones detect obstacles that they need to avoid. An issue that arises in obstacle avoidance is that drones can get stuck in front of an obstacle if they are unable to decide which direction to travel; in this work, the decision process is managed by combining two control modes that correspond to the two direction options available, using a robust switching algorithm to select which mode to use for each obstacle. This work extends the approach used in literature to include multiple obstacles and allow obstacles to be detected dynamically, enabling the drones to navigate through an unknown environment as they locate the target. The algorithms are demonstrated on unmanned drones in the VT SpaceDrones test facility, illustrating the capabilities and effectiveness of the methods presented in a series of scenarios.

A maneuvering reentry vehicle (evader) and missile interceptor (pursuer) engagement is modeled in two dimensions, with constant velocity vehicles subject to bounded accelerations. Parameterized guidance laws are constructed for both players and a continuous, but finite-dimensional game is formulated. The related Majorant and Minorant game formulations are analyzed. Several modifications are provided in order to smooth performance contours. A comparison of the Majorant and Minorant game formulation shows the game is not strictly determined and there is a significant advantage to knowing the opponent’s strategy. / Master of Science

With the ever increasing adoption of IPv6, there has been a growing concern for security and privacy of IPv6 networks. Mechanisms like the Moving Target IPv6 Defense (MT6D) leverage the immense address space available with the new 128-bit addressing scheme to improve security and privacy of IPv6 networks. MT6D allows participating hosts to hop onto new addresses, that are cryptographically computed, without any disruption to ongoing conversations. However, there is no feedback mechanism in the current MT6D implementation to substantiate the core strength of the scheme i.e., to find an attacker attempting to discover and target any MT6D addresses. This thesis proposes a method to monitor the intruder activity targeting the relinquished addresses to extract information for reinforcing the defenses of the MT6D scheme. Our solution identifies and acquires IPv6 addresses that are being discarded by MT6D hosts on a local network, in addition to monitoring and visualizing the incoming traffic on these addresses. This is essentially equivalent to forming a darknet out of the discarded MT6D addresses. The solution's architecture also includes an ability to deploy a virtual (LXC-based) honeypot on-demand, based on any interesting traffic pattern observed on a discarded address. With this solution in place, we can become cognizant of an attacker trailing an MT6D-host along the address changes, as well as understanding the composition of attack traffic hitting the discarded MT6D addresses. With the honeypot deployment capabilities, the solution can take the conversation forward with the attacker to collect more information on attacker methods and delay further tracking attempts. The solution architecture also allows an MT6D host to query the solution database for network activity on its relinquished addresses as a JavaScript Object Notation (JSON) object. This feature allows the MT6D host to identify any suspicious activity on its discarded addresses and strengthen the MT6D scheme parameters accordingly. We have built a proof-of-concept for the proposed solution and analyzed the solution's feasibility and scalability. / Master of Science

The Internet of Things (IoT) is composed of billions of sensors and actuators that have varying tasks aimed at making industry, healthcare, and home life more efficient. These sensors and actuators are mainly low-powered and resource-constrained embedded devices with little room for implementing IP security in addition to their main function. With the fact that more of these devices are using IPv6 addressing, we seek to adapt a moving-target defense measure called Moving Target IPv6 Defense for use with embedded devices in order to add an additional layer of security. This adaptation, which we call Micro-Moving Target IPv6 Defense, operates within IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) which is used in IEEE 802.15.4 wireless networks in order to establish IPv6 communications. The purpose of this defense is to obfuscate the communications between a sensor and a server in order to thwart a potential attacker from performing eavesdropping, denial-of-service, or man-in-the-middle attacks. We present our work in establishing this security mechanism and analyze the required control overhead on the wireless network. / Master of Science

Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments. / Master of Science / Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.

The use of brightness mode (B-mode) ultrasound (US), color Doppler (CD), and power Doppler (PD) can all help evaluate and characterize splenic lesions. A relatively new non-Doppler technology used to evaluate vasculature called B-Flow helps overcome certain limitations of CD and PD that affect visualization of blood flow. There are no studies describing the use of B-Flow characterizing splenic lesions in dogs. A total of 97 splenic lesions were evaluated. Splenic lesions that were larger than 2 cm, distorted the splenic capsule, or accompanied free fluid were significantly associated with malignancy. Lesions with tortuous internal vessels on CD or B-Flow were significantly associated with malignancy. Lesions with large internal vessels compared to external vessels on PD and B-Flow were significantly associated with malignancy. In conclusion, these B-mode and vascular characteristics on CD, PD and B-flow may help clinicians prioritize malignant etiologies over benign ones and prompt more aggressive diagnostic recommendations.

“Big-Box Nation: Target, Retail, and Creating the American Landscape” focuses on Minneapolis-based retailer Target Corporation to examine how big-box stores became a common feature of the American landscape, whether in rural Iowa or urban Boston. Retail emerged as one of the most powerful industries in the United States during the twentieth century by mastering the mass distribution counterpart to mass production. As retail’s influence grew, so too did its impact on American life and government. Such influence made retail an important part of twentieth-century urban planning, from downtown consumer palaces to postwar suburban shopping centers, and from urban revitalization projects to brownfield redevelopments. The rise of modern environmental legislation during the 1970s, however, posed an unprecedented threat to retailers. While other industries faced the full brunt of environmental regulation, retail—despite its large environmental impact from its promotion of auto-dependency and its connection of consumers with ecologically damaging global supply chains—largely escaped the state’s regulatory reach. Target promoted consumer choice as an American value, lobbied at Congressional hearings to avoid penalties under the Clean Air Act and other environmental legislation, partnered with the federal government to build favorable relations, and launched corporate public relations campaigns to promote itself as a public citizen and retail as an environmentally responsible industry. The dissertation is the first to examine the political and environmental history of retail. Historical studies on big-box stores have overwhelmingly focused on Walmart and its connection to the rise of the service economy, modern conservatism and rural, blue-collar neopopulism. Target’s midwestern origin in urban, liberal Minneapolis complicates previous narratives of big-box retail and reveals the ways in which business looked to the state as an ally in their rise. Environmental historians, on the other hand, have detailed many ways in which twentieth-century mass consumption affected landscapes in the United States and across the world, yet the material and domestic sites where Americans purchased the products made from the labor, energy, and resources of these distant places has not yet been examined as a window onto a unique form of environmental negotiation and corporate power. / 2026-09-18T00:00:00Z