Robotics Commons^™

The Coral Carousel: A Device And Method For In-Situ Propagation Of Deep-Sea Corals, Gregory Bales

Open Access Master's Theses

The research described herein covers the development of a tool station for performing in-situ propagation of corals using a work class ROV. This includes a system for manipulating coral fragments and affixing them to a cement base plug. To validate this tool station, testing was performed both in the lab as well as at depth in the Gulf of Mexico. As restoration of shallow-water corals has grown in popularity, many techniques have been developed for propagation. However, it is difficult or inappropriate to directly apply these techniques to deep-sea corals. While some forms of diving are capable of approaching the …

Toward Precise Long-Range Underwater Acoustic Geo-Positioning: Utilizing Vehicle Data And Deepening The Gnss Analogy Through Uncertainty Modeling, Isaac B. Salazar

Open Access Master's Theses

Electromagnetic signals, such as those used by Global Navigation Satellite Systems (GNSS), attenuate dramatically underwater, but acoustic signals can travel hundreds of kilometers, and can be used for positioning in much the same way. Concepts from GNSS can be applied to the subsurface context, as at a basic level, the principles of geo-positioning are identical. Key challenges in translating satellite positioning models to the underwater acoustic domain include differences in signal type as well as instrumentation and propagation environment. Acoustic signals travel at much slower speeds and are subject to significant environmental variability due to complex ocean dynamics. Here, the …

A Framework For Biomimetic Robot Design Applied To The Development Of A Robotic Model Of Drosophila Melanogaster, Clarissa A. Goldsmith

Graduate Theses, Dissertations, and Problem Reports (ETD)

For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, “biomimetic” robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. Much of this work involves biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which necessitate …

End-To-End Autonomous Quadcopter Using Reinforcement Learning, Mohamed Marwan Chawa

Theses and Dissertations

This thesis investigates the potential of Reinforcement Learning (RL) for achieving robust and adaptable quadcopter control, focusing on trajectory and attitude stabilization. We compare state-of-the-art RL algorithms, specifically Proximal Policy Optimization (PPO), against traditional Proportional-Integral-Derivative (PID) controllers across three tasks: hovering, slow trajectory following, and fast trajectory following. To enhance realism, we employ a modified PyFlyt simulation environment with a high-fidelity Crazyflie 2.x model, accounting for motor dynamics, noise, wind disturbances, and aerodynamic drag.

The challenge of operating a quadcopter can be divided into two distinct parts: planning a flight path and actually following that path. Our focus is on …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Pseudo Gps For Romi, Emmanuel Baez, Owen Guinane, Gabriel Coria, Conor Schott

Mechanical Engineering

The Pseudo-GPS system for Romi robots addresses the need for precise real-time location tracking in Cal Poly's Mechatronics lab. This project, developed by Emmanuel Baez, Gabriel Coria, Owen Guinane, and Conor Schott, under the guidance of instructor Charlie Refvem, provides a proof-of-concept system to enhance the Romi robots' geolocation capabilities for advanced robotic algorithms.

The proposed system uses a Raspberry Pi 4 equipped with a Pi camera module and ArUco markers to track the position and orientation of Romi robots within a lab environment. Custom 3D-printed stands secure markers on the robots, and a designated origin marker defines the coordinate …

Heterogeneous Collaborative Robotics: Multi-Robot Navigation In Dynamic Environments, Tyler Nicholas Raettig

Theses and Dissertations

Abstract: The challenges of multi-robot navigation in dynamic environments, focusing on uncertainties in obstacle complexities, partial observation, and the transition of policies from simulations to the real world. The proposed approach utilizes a deep reinforcement learning (DRL) framework enabling a Light Detection and Ranging (LiDAR)-equipped robot to communicate with a camera-equipped robot to achieve optimal paths despite their different sensors. The key contributions include the development of a cooperative architecture for information exchange between robots, a DRL-based framework for learning navigation policies, and a training mechanism based on dynamic randomization for enhanced real-world adaptability. Experimental validation using Gazebo simulations demonstrates …

Improving Robustness Of Learning-Based Approaches In Autonomous Systems And Engineering Education, Godwyll Aikins

Theses and Dissertations

This dissertation advances the development of robust learning-based approaches across two complementary domains: engineering education and autonomous systems. Through four studies, this research addresses critical challenges in preparing data-proficient engineers and developing reliable autonomous systems that can operate under uncertainty and incomplete information. The engineering education study examines how mechanical and aerospace engineering undergraduates conceptualize and develop data proficiency skills essential for modern engineering practice. Through interviews with 27 students, the research employs the How People Learn framework to analyze student perspectives on information literacy, data interpretation, and computational thinking. The findings inform pedagogical strategies for developing data proficiency in …

End-To-End Learning For A Low-Cost Robotics Arm, Abhishek Chothani

Theses and Dissertations

Robotic manipulation is a cornerstone of automation, with the ultimate goal of developing versatile systems capable of executing a wide range of real-world tasks autonomously. Traditional robotics approaches, while reliable and widely adopted in industrial settings, often struggle with adaptability, perception, and dynamic task execution. This thesis explores the evolution from classical robotics techniques to modern learning-based approaches, leveraging advancements in artificial intelligence to overcome these limitations.

Initially, the thesis presents a pick-and-place pipeline built using the Drake robotics framework and the KUKA iiwa robotic arm. This system employs a pseudoinverse controller for inverse kinematics to perform structured tasks like …

Computer Vision In A Robotic Arm, Jack Maxwell

College of Engineering Summer Undergraduate Research Program

We used a machine learning-based object detection algorithm to give a robotic arm the ability to "see" with its camera.

Design And Implementation Of An Inverted Short Baseline Acoustic Positioning System, Jakob Frabosilio

Master's Theses

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time.

A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

Studying The Performance Of Object Recognition With Fusion Of Visible Light And Infrared Images With Neural Networks, Plamen Petkov

Doctoral Dissertations and Master's Theses

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible …

Summonable Construction Delivery Robot, Kevin M. Lewis

Honors Capstones

In many different construction industries, there is a need for tools, parts, and other necessary items to be transported quickly and efficiently over various types of terrain. Human resources have often been used to address these needs, which can become very time and cost inefficient over long periods. The design proposal here is aimed at addressing this need by developing an autonomous outdoor mobile robot based on a quadrupedal robot design. This approach differs by incorporating a wheeled and quadrupedal hybrid actuation system that provides terrain negotiation and speed at the appropriate times. The team uses Robot Operating System (ROS) …

Experiment Development And Validation Of A Granular Jamming Robotic Gripper, Jacob R. Dowd

UNLV Theses, Dissertations, Professional Papers, and Capstones

A granular jamming gripper (GJG) is widely known as a Universal Gripper because of the wide range of objects that it can grasp and the simplicity of control, design, and manufacturing. Despite multitude of research improving the GJG, here, we focus on the base version of the GJG and attempt to glean the range of objects that it may reliably grasp. Despite the limited range of objects, which were a sphere, rectangular prism, and cylinder, we gleaned geometric properties as it relates to successful and unsuccessful grasping. This was based on the two types of testing: push and pull testing …

Deep Reinforcement Learning Of Variable Impedance Control For Object-Picking Tasks, Akshit Lunia

All Theses

The increasing deployment of robots in industries with varying tasks has accelerated the development of various control frameworks, enabling robots to replace humans in repetitive, exhaustive, and hazardous jobs. One critical aspect is the robots' interaction with their environment, particularly in unknown object-picking tasks, which involve intricate object weight estimations and calculations when lifting objects. In this study, a unique control framework is proposed to modulate the force exerted by a manipulator for lifting an unknown object, eliminating the need for feedback from a force/torque sensor. The framework utilizes a variable impedance controller to generate the required force, and an …

Sliding Markov Decision Processes For Dynamic Task Planning On Uncrewed Aerial Vehicles, Trent Wiens

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy

Mechanical Engineering ETDs

This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …

Trust-Based Variable Impedance Control And Passivity-Based Switched System Analysis For Human-Robot Cooperative Manipulation, Zhanrui Liao

All Dissertations

Human-robot cooperative manipulation (co-manipulation) is one of the most prominent human-robot collaboration (HRC) tasks, where humans and robots manipulate the same object. Trust in HRC is crucial in determining human acceptance of robots and, hence, robot utilization. A probabilistic dynamic Bayesian network (DBN) trust model that integrates a time-series trust model is presented in this thesis. The trust model is learned using a continuous and normalized Baum-Welch (BW) algorithm, devised to account for the continuous nature of trust evolution and the limitations of the classic parameter learning method. To ensure a good HRC in co-manipulation, a variable impedance control framework …

Data-Driven Predictive Modeling To Enhance Search Efficiency Of Glowworm-Inspired Robotic Swarms In Multiple Emission Source Localization Tasks, Payal Nandi

Mechanical & Aerospace Engineering Theses & Dissertations

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (< 10 agents) to larger robot teams (10 – 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO—a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling—to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance.