UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item APPLIED AERIAL ROBOTICS FOR LONG RANGE AUTONOMY AND ADVANCED PERCEPTION(2024) Cui, Wei; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the challenges of conducting autonomous long-distance operations in settings where communication is restricted or unavailable. It involves the development of aerial autonomy software, ground station user interface, and simulation tools. Field experiments are conducted to assess the real-world performance and scalability of the developed autonomous multi-vehicle systems. A search and revisit framework involving multiple UAS engaged in expansive area exploration has been developed. By employing the ARL MAVericks autonomy stack, we have devised three system designs with improving levels of autonomy. This approach is effective in developing autonomous system capabilities for extended-range missions, enhancing effectiveness in reconnaissance, search, and rescue missions. Furthermore, the dissertation introduces an innovative application of enhanced target detection and localization techniques tailored specifically for small UAS deployment. Neural network fine-tuning and AprilTag detector selection are carefully conducted. Augmented by a meticulously designed workflow for performance evaluation and validation, our approach aims to improve the precision of target detection and localization using a single RGB camera module. Additionally, the dissertation presents the implementation of a specialized ground control user interface. Functioning as a centralized command center, the user interface facilitates real-time monitoring and coordination of heterogeneous aerial and ground robotic platforms engaged in collaborative search missions. By streamlining air-ground coordination and human-robot interaction, the custom user interface optimizes the collective capabilities of diverse aerial and ground robotic platforms, enhancing overall mission effectiveness. The experimental results from multi-vehicle autonomous search missions, evaluating centralized and decentralized control in beyond visual line of sight scenarios, are presented, proving the efficacy of the search and revisit framework operating in real-world scenarios. Finally, the dissertation covers the design and implementation of a resilient network link tailored for robotic platforms operating in environments with limited bandwidth. This essential infrastructure enhancement is devised to overcome communication constraints, ensuring reliable data exchange, and strengthening the resilience of autonomous systems in bandwidth-limited environments.Item Minimal Perception: Enabling Autonomy on Resource-Constrained Robots(2023) Singh, Chahat Deep; Aloimonos, Yiannis; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mobile robots are widely used and crucial in diverse fields due to their autonomous task performance. They enhance efficiency, and safety, and enable novel applications like precision agriculture, environmental monitoring, disaster management, and inspection. Perception plays a vital role in their autonomous behavior for environmental understanding and interaction. Perception in robots refers to their ability to gather, process, and interpret environmental data, enabling autonomous interactions. It facilitates navigation, object identification, and real-time reactions. By integrating perception, robots achieve onboard autonomy, operating without constant human intervention, even in remote or hazardous areas. This enhances adaptability and scalability. This thesis explores the challenge of developing autonomous systems for smaller robots used in precise tasks like confined space inspections and robot pollination. These robots face limitations in real-time perception due to computing, power, and sensing constraints. To address this, we draw inspiration from small organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimalistic sensory and neural systems. This research aims to provide insights into designing compact, efficient, and minimal perception systems for tiny autonomous robots. Embracing this minimalism is paramount in unlocking the full potential of tiny robots and enhancing their perception systems. By streamlining and simplifying their design and functionality, these compact robots can maximize efficiency and overcome limitations imposed by size constraints. In this work, a Minimal Perception framework is proposed that enables onboard autonomy in resource-constrained robots at scales (as small as a credit card) that were not possible before. Minimal perception refers to a simplified, efficient, and selective approach from both hardware and software perspectives to gather and process sensory information. Adopting a task-centric perspective allows for further refinement of the minimalist perception framework for tiny robots. For instance, certain animals like jumping spiders, measuring just 1/2 inch in length, demonstrate minimal perception capabilities through sparse vision facilitated by multiple eyes, enabling them to efficiently perceive their surroundings and capture prey with remarkable agility. This thesis introduces a cutting-edge exploration of the minimal perception framework, pushing the boundaries of robot autonomy to new heights. The contributions of this work can be summarized as follows:1. Utilizing minimal quantities such as uncertainty in optical flow and its untapped potential to enable autonomous navigation, static and dynamic obstacle avoidance, and the ability to fly through unknown gaps. 2. By utilizing the principles of interactive perception, the framework proposes novel object segmentation in cluttered environments eliminating the reliance on neural network training for object recognition. 3. Introducing a generative simulator called WorldGen that has the power to generate countless cities and petabytes of high-quality annotated data, designed to minimize the demanding need for laborious 3D modeling and annotations, thus unlocking unprecedented possibilities for perception and autonomy tasks. 4. Proposed a method to predict metric dense depth maps in never-seen or out-of-domain environments by fusing information from a traditional RGB camera and a sparse 64-pixel depth sensor. 5. The autonomous capabilities of the tiny robots are demonstrated on both aerial and ground robots: (a) autonomous car with a size smaller than a credit card (70mm), and (b) bee drone with a length of 120mm, showcasing navigation abilities, depth perception in all four main directions, and effective avoidance of both static and dynamic obstacles. In conclusion, the integration of the minimal perception framework in tiny mobile robots heralds a new era of possibilities, signaling a paradigm shift in unlocking their perception and autonomy potential. This thesis would serve as a transformative milestone that will reshape the landscape of mobile robot autonomy, ushering in a future where tiny robots operate synergistically in swarms, revolutionizing fields such as exploration, disaster response, and distributed sensing.Item Assured Autonomy in Multiagent Systems with Safe Learning(2022) Fiaz, Usman Amin; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomous multiagent systems is an area that is currently receiving increasing attention in the communities of robotics, control systems, and machine learning (ML) and artificial intelligence (AI). It is evident today, how autonomous robots and vehicles can help us shape our future. Teams of robots are being used to help identify and rescue survivors in case of a natural disaster for instance. There we understand that we are talking minutes and seconds that can decide whether you can save a person's life or not. This example portrays not only the value of safety but also the significance of time, in planning complex missions with autonomous agents. This thesis aims to develop a generic, composable framework for a multiagent system (of robots or vehicles), which can safely carry out time-critical missions in a distributed and autonomous fashion. The goal is to provide formal guarantees on both safety and finite-time mission completion in real time, thus, to answer the question: “how trustworthy is the autonomy of a multi-robot system in a complex mission?” We refer to this notion of autonomy in multiagent systems as assured or trusted autonomy, which is currently a very sought-after area of research, thanks to its enormous applications in autonomous driving for instance. There are two interconnected components of this thesis. In the first part, using tools from control theory (optimal control), formal methods (temporal logic and hybrid automata), and optimization (mixed-integer programming), we propose multiple variants of (almost) realtime planning algorithms, which provide formal guarantees on safety and finite-time mission completion for a multiagent system in a complex mission. Our proposed framework is hybrid, distributed, and inherently composable, as it uses a divide-and-conquer approach for planning a complex mission, by breaking it down into several sub-tasks. This approach enables us to implement the resulting algorithms on robots with limited computational power, while still achieving close to realtime performance. We validate the efficacy of our methods on multiple use cases such as autonomous search and rescue with a team of unmanned aerial vehicles (UAVs) and ground robots, autonomous aerial grasping and navigation, UAV-based surveillance, and UAV-based inspection tasks in industrial environments. In the second part, our goal is to translate and adapt these developed algorithms to safely learn actions and policies for robots in dynamic environments, so that they can accomplish their mission even in the presence of uncertainty. To accomplish this goal, we introduce the ideas of self-monitoring and self-correction for agents using hybrid automata theory and model predictive control (MPC). Self-monitoring and self-correction refer to the problems in autonomy where the autonomous agents monitor their performance, detect deviations from normal or expected behavior, and learn to adjust both the description of their mission/task and their performance online, to maintain the expected behavior and performance. In this setting, we propose a formal and composable notion of safety and adaptation for autonomous multiagent systems, which we refer to as safe learning. We revisit one of the earlier use cases to demonstrate the capabilities of our approach for a team of autonomous UAVs in a surveillance and search and rescue mission scenario. Despite portraying results mainly for UAVs in this thesis, we argue that the proposed planning framework is transferable to any team of autonomous agents, under some realistic assumptions. We hope that this research will serve several modern applications of public interest, such as autopilots and flight controllers, autonomous driving systems (ADS), autonomous UAV missions such as aerial grasping and package delivery with drones etc., by improving upon the existing safety of their autonomous operation.Item Planning for Autonomous Operation of Unmanned Surface Vehicles(2016) Shah, Brual; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14.Item THE RELATOINSHIP BETWEEN TEACHER PERCEPTIONS OF AUTONOMY IN THE CLASSROOM AND STANDARDS BASED ACCOUNTABILITY REFORM(2012) Sparks, Dinah; Malen, Betty; Croninger, Robert; Education Policy, and Leadership; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the past 30 years, standards based accountability reform (SBA) has taken hold in public education. SBA reform includes defined academic expectations, curricula standards, measureable assessments, and performance accountability. SBA impacts multiple levels of public education. Its most recent federal codification, the No Child Left Behind (NCLB) Act, includes sanctions meant to influence what happens in classrooms. Historically, teachers have held a great deal of control over the activities in the classroom. Research suggests that teacher control (i.e. autonomy) over the classroom often resulted in uneven implementation of reform policies across schools, the transformation of policies to fit existing practice or the insulation of classrooms altogether from policy reform. To achieve its stated goals, SBA seeks to influence teacher and school practices, particularly where students fail to meet performance goals. This study examines the intersection of teacher perceptions of autonomy and SBA reforms, including NCLB. The study uses four waves of nationally representative Schools and Staffing Survey data from 1993-94 to 2007-08 to investigate changes in teacher autonomy over time and to examine specific school and teachers characteristics associated with changes in autonomy in 2007-08. Over-time findings reveal that teachers perceived lower classroom autonomy between 2003-04 and 2007-08. Across all four waves of data, the variation in teachers' classroom autonomy increased, and more of this increased variation occurred between schools rather than within schools. Findings for 2007-08 reveal that teachers who taught in elementary schools or taught tested subjects perceived lower levels of autonomy than did teachers in secondary schools or who taught non-tested subjects. Further analyses based on state application of adequate yearly progress (AYP) sanctions revealed a differential effect on teacher autonomy for Title I schools and for schools that failed to make AYP. Findings from this study suggest that although NCLB targets Title I schools, teachers in all schools perceive lower autonomy based on the grade level and the subject matter taught, and that state policies regarding NCLB may lead to uneven or unintended effects on teacher perceptions of autonomy in the classroom.Item Children's Music in the Southern Baptist Convention: An Ethnographic Study of Four Churches in Maryland Examining the Effects of Doctrine and Local Church Autonomy on Children's Music(2011) Diab, Melak Victoria; Provine, Robert C; Music; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Southern Baptist Convention (SBC) is the largest Protestant denomination and the largest group of Baptists in the United States. Furthermore, LifeWay Christian Resources, the Southern Baptist publishing house, is the largest Christian publisher in the United States, producing various literature and media resources, including music material for children. However, the autonomous nature of the local Baptist church gives it absolute freedom to choose programs and materials apart from the Southern Baptist National Convention and LifeWay. This study examines the dynamics of the relationship between the National Convention and the local church as it pertains to children's music. The study looks at the theological and organizational framework on the national level and the local church level and how they affect children and children's music in an autonomous local church setting. The study reveals that all resources and programs related to children on the local church and national convention level, such as children's choir and Vacation Bible School, and Sunday school, are directed towards teaching the children about the two most fundamental concepts of the faith, these are conversion (how to become a Christian) and worship (how to commune with God). The SBC curriculum for children is undergirded by Howard Gardener's theory of multiple intelligences, and makes extensive use of creative movement and American Sign Language to capture children's attention. However, the nature of local church autonomy gives each church the freedom to tailor SBC curriculum to its specific needs or to choose a curriculum from another denomination altogether.