Computer Engineering Commons^™

Under Pressure: The Soft Robotic Clap-And-Fling Of Cuvierina Atlantica, Daniel Mead

USF Tampa Graduate Theses and Dissertations

Evolution over billions of years has led to unique animals of all types. The tiny sea butterfly is one that remains mostly anonymous because of its size and low place on the food chain, but it is a swimming creature that when examined closely reveals a surprise. Shockingly, it has a swimming motion nearly identical to small insects flying at intermediate Reynolds numbers, referred to as the clap-and-fling. The centimeter long pteropod flapping briskly at 5Hz escapes attention with its size and speed. Modeling the clap-and-fling motion of the sea butterfly at a larger scale allows the benefits and uniqueness …

Love Machina, John C. Lyden

Journal of Religion & Film

This is a film review of Love Machina (2024), directed by Peter Sillen.

Securing Edge Computing: A Hierarchical Iot Service Framework, Sajan Poudel, Nishar Miya, Rasib Khan

Posters-at-the-Capitol

Title: Securing Edge Computing: A Hierarchical IoT Service Framework

Authors: Nishar Miya, Sajan Poudel, Faculty Advisor: Rasib Khan, Ph.D.

Department: School of Computing and Analytics, College of Informatics, Northern Kentucky University

Abstract:

Edge computing, a paradigm shift in data processing, faces a critical challenge: ensuring security in a landscape marked by decentralization, distributed nodes, and a myriad of devices. These factors make traditional security measures inadequate, as they cannot effectively address the unique vulnerabilities of edge environments. Our research introduces a hierarchical framework that excels in securing IoT-based edge services against these inherent risks.

Our secure by design approach prioritizes …

A Framework-Based Cross-Institutional Cpd For Academic Staff In Gen-Ai Literacy, Critical Inquiry And Authentic Assessment, Roisin Donnelly, Ita Kennelly

Books/Book Chapters

Generative-Artificial Intelligence (Gen-AI) has emerged as a transformative force profoundly influencing, if not revolutionizing the way we now teach and how students learn in higher education (HE). Despite the initial flurry of early research studies following the raising of public awareness of Gen-AI (and in particular ChatGPT), enduring pragmatic questions remain for academic staff on how best to protect and promote student learning, how to meaningfully support assessment integrity from a curriculum perspective, and additionally how to effectively use Gen-AI technologies to aid learning and foster deeper critical thinking.

Nonlinear Guidance And Control Of Unmanned Aerial Manipulators For Delivering A Payload On A Moving Platform, Ravi Gyawali

Mechanical and Aerospace Engineering Theses - Archive

Unmanned Aerial manipulators (UAMs) are a class of Unmanned Aerial Vehicles (UAVs) equipped with a manipulator. By combining the aerial mobility of a UAV with a manipulator's dexterity, these hybrid systems can perform a wide range of complex tasks while reducing risks and costs. As a result, they are increasingly being utilized for military, industrial, and agricultural applications.

The thesis presents a novel approach for a multi-UAM system to collaboratively deliver a payload on a stationary or maneuvering platform. A sliding-mode-based guidance law, sourced from existing literature, is integrated with a combined control technique for the UAVs and their respective …

Algorithms For Safe And Robust Motion Control Of Autonomous Ground Robots And Vehicles, Manavendra Desai

Wayne State University Dissertations

This dissertation explores, investigates, develops and experimentally validates algorithms for safe and robust motion control of ground robots and vehicles. The emphasis is on design of controllers for ground autonomy with safety either embedded by design or enforced by appropriately wrapping a safety net around an existing nominal controller.

Ground robots have been considered for their importance in compensating for labour or personnel shortage for material handling in the manufacturing, logistics and healthcare sectors. On the other hand, ground vehicles have been considered for the existing potential of making driver assistance systems safer and more intelligent, particularly in at-the-limit driving …

Manifold Learning In Robotics: A Tutorial And Survey, Marcus Hawkins

Computer Science and Engineering Theses - Archive

In this article, we hope to represent the current state of the art of manifold learning in an understandable and approachable way. The authors will present a general overview core algorithms associated with linear and nonlinear dimensionality reduction techniques, give rudimentary definitions from differential geometry, and tenets of robotic perception, manipulation and path planning. Some of the historical applications of these algorithms will be presented, as well as conjectures about future uses, through examples from peer-reviewed journals.

A Learning-Based Framework For Autonomous Robotic Operations In Resource-Denied Environments, Joseph M. Cloud

Computer Science and Engineering Dissertations - Archive

Establishing a sustained human presence beyond Earth necessitates the development of autonomous systems capable of extracting and utilizing local resources. On the Moon, in-situ resource utilization (ISRU) is essential to reduce the dependency on Earth-based supplies. Leveraging lunar resources such as water ice for life support and fuel production or regolith for surface construction will enable long-term lunar missions and future deep space exploration. NASA's Artemis program is targeting the lunar south pole (LSP), an area with raw, yet abundant resources available. The harsh environmental conditions present significant challenges for humans operating and installing surface infrastructure. Autonomous robotic systems are …

Magnetometer-Less State-Estimation Of A Mobile Robot Using Cascaded Kalman Filters, Tommy Le

Graduate Research Theses & Dissertations

Localization, or state-estimation algorithms, are one of the most important aspects inthe development of autonomous mobile robots. Typical localization requires an IMU (Inertial Measurement Unit) along with an external reference, such as GPS (Global Positioning System) for outdoor applications. In indoor applications, the GPS data is not accessible so many mobile robot implementations turn to magnetometers to provide additional pose information. However, in the context of miniaturizing robotic systems, magnetometers are not always reliable due to their proximity to motors and other electronics, causing magnetic distortion and in turn, incorrect pose information. To address this issue, this thesis proposes a …

Joint Learning Of Unknown Safety Constraints And Control Policies In Reinforcement Learning, Lunet Abiye Yifru

Graduate Theses, Dissertations, and Problem Reports (ETD)

Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. However, this reliance on predefined safety constraints poses limitations in dynamic and unpredictable real-world settings where such constraints may not be available or sufficiently adaptable. Bridging this gap, we propose a novel approach that concurrently learns a safe RL control policy and identifies the unknown safety constraint parameters of a given environment. …

An Approach For Robotic Pollination That Utilizes Imitation Learning, Ronald Michael Butts Ii

Graduate Theses, Dissertations, and Problem Reports (ETD)

The global decline in pollinator populations poses a significant threat to agriculture, motivating the development of robotic pollination systems. Previous works demonstrated successful robotic pollination of bramble flowers using visual servoing; however, pollination was limited to specific flower orientations. As such, the objective of this work is to develop a robotic pollination system that is capable of pollinating a wider range of orientations.

This research introduces an imitation learning-based framework for robotic pollination that positions the manipulator to view chosen flowers in specific orientations. The developed model leverages object detection (YOLOv8) to identify individual flowers and a convolutional neural network …

Observability-Aware Path Planning For Autonomous Navigation, Raymond Brink Neistat

Open Access Master's Theses

Terrain-Aided Navigation (TAN) is a popular method of localization for GPS-denied vehicles, particularly in the marine domain. There are many ways to perform TAN in a marine setting, such as Bathymetric SLAM (BSLAM) and Bayesian Filtering with the aid of an a priori map. These techniques have been studied extensively, but show an overall lack of rigorous observability analyses. Without nonlinear observability analyses, TAN practitioners do not have an analytical indicator to know which areas of terrain will provide opportunities for the best localization performance. This thesis reviews current developments in the endeavors of nonlinear observability analyses as well as …

Enhancing Robotic Exploration Through Semantically-Guided Sampling Strategies, Christopher Alexander Arend Tatsch

Graduate Theses, Dissertations, and Problem Reports (ETD)

From space and deep-sea exploration to disaster response and environmental monitoring, autonomous robots are essential for advancing science, improving safety, and addressing critical challenges. This dissertation introduces a novel open-source strategy for autonomous robotic exploration: the Semantically-Guided Exploration (SGE) framework. Designed for ground vehicles, SGE integrates semantic understanding into the autonomous exploration process, improving decision-making in complex environments. Specifically, the proposed sampling-based approach uses the information from the semantic segmentation of RGB images and depth images to guide the robot's selection of exploration goals. This method enables the robot to steer away from potential dangers such as large rocks and …

Children's Hospital Animatronic, Erin Keller, Wesley Cunningham, Dylan Mueller, Carl Richter

Williams Honors College, Honors Research Projects

This senior project explores the development of a pediatric-oriented animatronic designed to enhance the hospital experience for children. Recognizing the importance of alleviating the anxiety and fear that often accompany hospital visits, the project focuses on creating an engaging and interactive companion to soothe children.

Key components of this project include:

1. Creative Design: The animatronic character's design prioritizes child-friendliness, employing research into child psychology and preferences to ensure an appealing and approachable aesthetic.

2. Electrical and Mechanical Engineering: A robust mechanical and electronic system was engineered to enable lifelike movements, gestures, and responses.

3. Interactive Features: A simple, user-friendly app will be …

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Development Of A Solidworks Api, Andrew Magyar

Williams Honors College, Honors Research Projects

With the current advancements in CAD software, and precision of modern motors, we believe that a new way to communicate from hardware to software is possible. This report details the steps we have taken to attempt just that. The essential idea behind this is that modern controls for robotics are not as user friendly as they can be. The general action taken is to create an API for SolidWorks that connects it to the other necessary controller software and makes it so that a robot, and its exact current position, can be changed simply and intuitively from SolidWorks. Furthermore, the …

Autonomous Damage And Structure Scanning Drone, Natasha Ninan, Amber Long, Lee Nestor, Emmanuel Jensen

Williams Honors College, Honors Research Projects

Remote damage analysis plays a crucial role in lowering the risk associated with human presence at dangerous sites. This project focuses on developing a system for remote structural examination using photogrammetric techniques and analysis. The system utilizes a drone equipped with cameras for photogrammetry, and LiDARs for navigation. This setup can enable efficient structural model generation with the collection of images from multiple points. A novel structural analysis is performed to detect potential damage or points of failure in the structure.

Key components include a teleoperated drone, a software pipeline for photogrammetric analysis, collision protection mechanisms, and a user interface. …

Modeling And Control For Precision Robotic Machining, Patrick Bazzoli

Doctoral Dissertations

"Robots are used in a wide variety of manufacturing applications, but machining applications in which robots can excel are limited by their lower accuracy and stiffness relative to traditional CNC machines. This work is composed of two parts: one to evaluate a robot’s accuracy and one to compensate for the vibrations of the robot due to its lower stiffness.

In order to evaluate whether a robot has the necessary accuracy to perform a given machining task, Paper 1 discusses a novel Model Invalidation method. This methodology provides a statistical framework as well as a measurement strategy for determining if a …

Autonomous Basketball Court Creation Robot, Bryce Haldeman, Tyler Gray, Dalon Vura

Williams Honors College, Honors Research Projects

The Autonomous Basketball Court Outlining System presents a comprehensive solution for precision court marking. Powered by a 24V lithium-ion battery and driven by a single ST microcontroller, the system autonomously marks the outline of a half basketball court using predefined algorithms. User-friendly features include easy loading of marking material, actuated by gravity or a small servo motor depending on material of choice, ensuring intuitive operation. Safety is prioritized, with the servo motor eliminating high-pressure concerns, and the system maintains a controlled speed accounting for user well-being. Two step and direction servo motors enable accurate linear displacement, facilitating straight lines, and …

Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis

Theses and Dissertations

This dissertation explores how to better manage resources in mobile networks, especially for enhancing the performance of Unmanned Aerial Vehicles (UAV)-supported IoT networks. We explored ways to set up a flexible communication architecture that can handle large IoT deployments by making good use of mobile core network resources like bearers and data paths. We developed strategies that meet the needs of IoT networks and enhance network performance. We also developed and tested a system that combines traffic from several mobile devices that use the same user identity and network resources within the core mobile network. We used everyday smartphones, SIM …

Shape Inverse Prediction Of Magnetic Field-Actuated Soft Robots By Neural Network Machine Learning, Lineth J. Perez Monsalve

Theses and Dissertations

Soft robotics has drawn tremendous interest in recent years because the compliance and motion of soft robotics enable biocompatibility and versatility for many applications, such as human-machine interaction, wearable and assistive devices, and health monitoring. This study introduces a novel predictive modeling approach using neural networks for shape control of magnetic soft robots. The robots are made of silicone materials embedded with hard magnetic particles, which respond to the external magnetic field provided by a ring-type of permanent magnet. These robots, free from physical connections to external devices, i.e., non-tethered actuation, hold significant potential for applications in healthcare, such as …

Initial Solution Improvements To A Tethered Robotic Path Planner Using Random Geometric Graph Configurations And Biased Sampling, Austen J. Goddu

Dissertations, Master's Theses and Master's Reports

With NASA's ongoing efforts to establish a presence on the lunar surface to eventually move on to exploring mars, the development of intelligent robotic systems is more important than ever. The ability of robotics to explore hazardous and extreme terrain, coupled with long communication times from earth places an ever increasing need on more robust and efficient autonomy. Tethered robotics offer unique advantages to explore scientific targets both on the lunar and martian surfaces, capable of using their tether as a physical or metaphorical lifeline to allow the exploration of slopes, extreme dark regions, or areas in which wireless communication …

On Uncertainty For Ill-Posed Robot Decision Problems, Jared Joseph Beard

Graduate Theses, Dissertations, and Problem Reports (ETD)

As robots adopt more real world responsibilities, they will be expected to solve more complicated problems. In some cases limited prior knowledge will result in unmodelled environmental conditions; in others, multiple users may have competing perspectives on how to frame a decision problem. Many existing frameworks, namely Markov decision processes (MDP) presuppose users have identified a specific problem with models sufficient to solve or learn a problem. If we wish to extend MDPs to novel problems or those heavily dependent on user feedback, autonomous decision makers must be able to identify limitations in how a given problem is framed and …

Exploring End-User Environments For The Control And Programming Of Collaborative Robots, Luiz Felipe Fronchetti Dias

Theses and Dissertations

To collaborate with the ongoing development of robotics, this thesis highlights three research contributions to collaborative robot programming. The first study evaluates block-based programming as an alternative for two-armed robots. A commercial solution is put in contrast with a block-based programming language. Both programming solutions are evaluated by 52 participants in an experiment involving a pick-and-place task. This study brings insights into human-robot collaboration, including robot positioning and interaction challenges. The second study discusses using mixed-reality devices as a potential workaround to the manual positioning of industrial and collaborative robots. Five different control interfaces implemented in mixed reality were used …

A One-Wheeled Robot For Exploring Rolling Disk Locomotion, David H. J. Schmidt

Graduate Research Theses & Dissertations

The work proposed in this thesis is motivated by observations of one-wheel vehicles called monocycles. Whereas the unicycle has the rider sitting on a seat above the wheel, the monocycle’s rider sits inside a large circular hoop that serves as the wheel. Due to the position of the rider inside the wheel, it lowers the overall center of mass of the vehicle below the center of the wheel. For this reason, the uncontrolled longitudinal (forward/backward, or drive axis) dynamics are stable. For modest speeds above a certain threshold, the lateral (side-to-side, or lean axis) dynamics of the monocycle are also …

Self-Supervised Contrastive Learning Using Eeg Signals For Mental Stress Assessment, Ugochukwu Uraechu

College of Graduate Studies: Theses & Dissertations

A study is presented to investigate self-supervised contrastive learning (SSCL) models using physiological data obtained from non-invasive wearable sensors for mental stress assessment. The present work involved acquisition of electroencephalography (EEG) signals using wearable sensors, signal preprocessing, data augmentation, and investigation of self-supervised contrastive learning (SSCL) algorithms for multi-class mental stress assessment. Seven volunteers participated in this study executing various mental tasks while wearing an OpenBCI head cap to acquire EEG signals. The acquired EEG signals were preprocessed and utilized for data augmentation in time and frequency domains with different SSCL models. Optimal data augmentation combinations and SSCL models were …

Learned Modeling And Control Of Continuum Robots For Surgical Applications, Cameron Jack Wolfe

Dartmouth College Master’s Theses

To reduce morbidity and mortality during surgery, surgeons have increasingly turned to Minimally Invasive Surgery (MIS), which involves passing instruments through small incisions or natural orifices to minimize patient trauma. Although MIS has significantly improved patient outcomes, it hinders a surgeon's dexterity and impairs visual and tactile feedback. These deficiencies have prompted the adoption of Robot-Assisted Surgery (RAS), in which surgeons control robots instead of using handheld instruments. While RAS has improved patient outcomes, robots struggle to navigate constricted spaces due to their rigidity, spurring the development of Continuum Robots (CRs). These flexible infinite degree-of-freedom robots move by bending, allowing …

External Runtime Execution Monitoring Of A Cyber Physical System Via Trace Interfaces, Peter Vaughan Truslow

Theses and Dissertations

In the past two decades, Unmanned Aerial Systems have progressed from expensive military hardware or one-off custom builds, to include off-the-shelf drones that can be purchased for a rather affordable price and flown by nearly anyone. As the technology and performance have improved, the door is opened to applications that require operation in environments where the consequences for failure are high, such as operating in the navigable airspace or in urban environments, or with human passengers. This requires a great deal of trust in the reliability and integrity of the control systems of the aircraft. A method of monitoring the …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Performance Characterization And Optimization Of A Point-Cloud-Based Path Planner In Off-Road Terrains, Casey D. Majhor

Dissertations, Master's Theses and Master's Reports

In this dissertation, I present a multifaceted study on the generation of artificial terrains using a multifractal method, and their use in autonomous ground vehicle (AGV) planning and navigation performance characterization in high-fidelity simulations, and automated parameter optimization.

My first contribution is a multifractal artificial terrain generation method leveraging the 3D Weierstrass-Mandelbrot function to control terrain roughness. We generate 60 unique off-road terrains while varying the fractal dimension and test the impact on vehicle traversal difficulty. Results show that increasing the fractal dimension decreases low-roughness areas, increases semi-rough and high-roughness areas, and decreases vehicle success rates while increasing vertical accelerations, …

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …