Engineering Commons^™

Safe Real-Time Obstacle Detection And Navigation Using Cbf–Clf And Cbf–Pid Control, Nicolas M. Hernandez

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traditional robotic navigation pipelines typically follow a three stage architecture: obstacle detection, path planning, and low-level control for trajectory tracking. While effective in static environments, these methods often introduce latency and lack formal guarantees of safety in dynamic or unplanned for scenarios. Our work addresses these limitations by developing a real-time controller grounded in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs), unified through a Quadratic Program (QP). We first investigate a hybrid CBF-PID-QP controller on a 1/10 scale car, where the CBF serves as a real-time safety filter, modifying the PID output to prevent constraint violations. While this …

Design, Modeling, Control, And Analysis Of A Modular Two-Wheeled Self-Balancing Robotic System, Mishek Jair Musa

Graduate Theses and Dissertations

Two-wheeled self-balancing robots have garnered substantial attention within the realms of research and innovation in academia and industrial settings. In particular, advances in control algorithms, machine learning, reinforcement learning, and sensor technologies have played a pivotal role in their development. Although primarily recognized for their utility in personal transportation in the commercial sector, these robots possess potential applications across various domains, including search and rescue, healthcare, material handling, logistics, etc., due to their active stabilization and exceptional maneuvering capabilities. A particular area of interest lies in the creation of modular self-balancing robots designed for seamless reconfiguration and enhanced human interaction. …

Integration And Testing Of A Quadruped Robot With Ros2, Jeremy S. West

Master's Theses

The Cal Poly Legged Robotics Group has been developing research and teaching platforms for agile legged robotics since 2020. These platforms are expected to provide students with opportunities to develop complete legged-robot systems from low-level control to advanced robotics tasks such as motion planning and decision making. However, the current prototyped quadruped robot lacked the software and sensing capabilities for high-level quadrupedal gaits and advanced robotic research.

To address these challenges, this project developed Switch, a robotic platform that builds upon the previous BRUCE platform with significant hardware and software upgrades. Switch features a modular design that allows individual software …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

Enhancing Emotional Accuracy And Behavioral Novelty In Robot Behaviors: A Generative-Discriminative Approach, Rista Baral

Boise State University Theses and Dissertations

Recent advancements in language modeling have improved robotic emotion expressiveness, yet several challenges remain. Many existing robotic expression models rely on fixed rules and static frameworks, which limit their ability to capture the dynamic nature of emotional expression. Additionally, these systems often struggle to balance emotional accuracy with generating novel and varied behaviors. These limitations underscore the need for method capable of delivering both emotionally accurate and diverse robot behaviors.

In this thesis, we addresses these challenges by introducing a new framework for expressive behavior generation in robots. We develop a generative model that generates robot behavior sequences that align …

Advancing Smart And Adaptive Living Spaces Through Design And Development Of Reconfigurable Multifunctional Robot Rooms, Nithesh Kumar

All Dissertations

This work explores and introduces prototype hardware for a new category of robots: ‘Robot Rooms’, in an effort to refine the concept of traditional smart spaces and human-robot interaction. Unlike traditional robots that tend to be compact and exist within a space, this new category of robots is designed to be expansive: they do not exist within a space but rather shape space around them. We present several design concepts for potential robotic elements of a Robot Room. We then develop and demonstrate, at full scale, a new and novel concept: a ‘slice’ of a Robot Room. This slice changes …

Flysurf: A Flying Robotic Surface With Shape Morphing And Motion Control, Kevin Aubert De Luchi Lomellini

Electrical and Computer Engineering ETDs

This thesis introduces FlySurf, a novel flying robotic surface capable of simultaneous shape morphing and motion control. FlySurf is modeled as a mesh-based dynamic structure actuated by multiple unmanned aerial vehicles, enabling complex deformations during flight. The proposed control architecture comprises three main components. First, a state estimator to infer the robotic surface state from limited observations. Second, a shape trajectory planner that incorporates an affine deformation term to preserve surface integrity during shape transitions. Third, a surface controller to minimize the error between the desired and actual surface configurations.

The methodology is validated through realistic simulations and hardware experiments, …

Benchmarking Model Predictive Control And Reinforcement Learning-Based Control For Legged Robot Locomotion In Mujoco Simulation, Shivayogi Akki, Tan Chen

Michigan Tech Publications

Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task, straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain …

Hierarchical Reinforcement Learning (Hrl) In Multi-Goal Spatial Navigation With Autonomous Mobile Robots, Brendon Johnson

USF Tampa Graduate Theses and Dissertations

Hierarchical reinforcement learning (HRL) is hypothesized to be able to take advantage of the inherent hierarchy in robot learning tasks with sparse reward schemes, in contrast to more traditional reinforcement learning algorithms. In this research, hierarchical reinforcement learning is evaluated and contrasted with standard reinforcement learning in complex navigation tasks. We evaluate unique characteristics of HRL, including their ability to create sub-goals and the termination function. We constructed experiments to test the differences between PPO and HRL, different ways of creating sub-goals, manual vs automatic sub-goal creation, and the effects of the frequency of termination on performance. These experiments highlight …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …

Ergonomic Human Robot Handovers Using Surface Electromyography (Semg) Sensors, Maya Murphy, Michael Mishkanian

Electrical and Computer Engineering Senior Theses

For decades, robots have been kept in cages in industry. With the advances of collaborative robots and Artificial Intelligence (AI), there is a shift towards humans and robots working together. In this research, we propose an ergonomically friendly collaborative robotic cell that enables a human and a collaborative robot to work synergistically to assemble a mobile robot. The collaborative robot provides the parts while explaining the process through a computer, and the human co-worker follows the instructions to complete the assembly. The proposed collaborative robotic cell is evaluated in a user study to ensure that the handovers of the parts …

Autonomous Mapping Rover, Garrett Jones, Timothy Kyle Chu, Eugenio Caruso Pasos

Electrical Engineering

This report details the design, implementation, and evaluation of "Rovero," an autonomous mapping rover developed as a senior project. Rovero integrates state-of-the-art technologies, including ROS2 for communication, SLAM algorithms for mapping, and sensor fusion for accurate navigation. The project aimed to achieve robust autonomous operation in indoor environments, leveraging a combination of LiDAR, IMU, and encoder data for real-time decision-making. Key challenges addressed include path planning, obstacle avoidance, and integration of multiple sensor inputs. Results demonstrate successful mapping capabilities and efficient navigation performance.

A Turtlebot3 Hardware Testbed For Distributed Kalman Filter Localization, Dmitri Dobrynin, Indigo T. Garcia

Electrical Engineering

This report presents the preliminary design for a distributed localization framework for a multi-robot system. Many robotics research papers provide simulations of proposed algorithms in regards to formation control and task allocation. However, it is often that these proposals are without hardware experiments, being limited only to simulation. The objective of this framework is to provide a hardware implementation of a distributed Kalman filtering algorithm for multi-agent localization, as well as provide grounds for future multi-agent experiments. The framework is implemented on a swarm of three Turtlebot3 mobile robots. The robots can accurately localize themselves with respect to other agents …

Distributed Formation Control Of Nonholonomic Mobile Robots: Safety-Critical Leader-Follower Approach With Obstacle Avoidance And Dynamic Reconfiguration, Kelvin C. Villago

Master's Theses

Networked control systems for multi-agent robotics have emerged as a critical paradigm for executing complex coordinated tasks in diverse environments. While formation control serves as the backbone of such systems, real-world deployment introduces significant challenges including communication constraints, environmental obstacles, and the need for adaptive reconfiguration. This research addresses these challenges by developing a novel unified framework that seamlessly integrates obstacle avoidance algorithms with dynamic formation reconfiguration capabilities, specifically designed for communication-limited networked control architectures. The proposed framework represents a significant advancement over existing approaches by simultaneously handling both static and dynamic obstacles while maintaining system cohesion under communication constraints. …

Development Of A Novel Bio-Mimetic Ornithopter With Variable Flapping Angle, Geourg Kivijian

Master's Theses

From the beginnings of flight, flapping-winged flight has been a goal of many engineers to mimic and replicate. With gains in aerodynamic efficiency of flight and certain other favorable characteristics such as reduced noise, higher maneuverability, and surveillance opportunities in urban environments, flapping-wing aerial vehicle(FWAV) designs are consistently pursued in many corners of academia and industry. This thesis goes into the development of a flapping wing aerial vehicle with a Bio-mimetic novel drive mechanism that is cable-driven and can produce variable amplitude and frequency flapping stroke. Employing a Field-Oriented Control (FOC) Brushless Direct Current (BLDC) motor, paired with a bio-mimetic …

Terraincraft: Automated Land-Cover–Driven Terrain Generation For Marine Robot Simulations, Xinyue Liang

Dartmouth College Master’s Theses

From self-driving cars navigating city streets to all-terrain vehicles tackling rugged landscapes, recent leaps in robotic autonomy due to fast pace development in deep learning are reshaping how machines interact with the real world. However, autonomy in the aquatic environment is still limited, due to difficulty in testing and unavailability of realistic simulation environments.

In this project, we aim to create an automated system that simplifies the processes of creating synthetic datasets for marine robots navigation training tasks. We achieved this through a land cover map controlled terrain generation. Our goal is to provide an automatic terrain generation system that …

Transfer Learning For Temporal Logic Objectives, Lucas M. Santana Rovira

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reinforcement learning algorithms can enable autonomous systems to learn the control skills needed to accomplish a task specified by a linear temporal logic formula. However, they cannot be transferred to a new task, even when the two are very similar. For each new task, the policy must be redesigned from scratch, which is a common limitation of existing reinforcement learning methods for temporal logic tasks. A proposed solution to this problem leverages the similarity between past and new tasks to reuse already learned control skills to accomplish the new task, with minimal or no retraining.

Rather than learning a single …

Untethered Isoperimetric Robotic Truss For Lunar Applications, Mihai Stanciu, Spencer Stowell, Isaac Weaver, Adam Rose, Chris Paul, James Wade, Ashleigh Cerven, Annie O'Bryan, Brian Bodily, Logan Yang, Nathan Usevitch

Faculty Publications

We introduce a robotic system designed to function as a lightweight, modular, and reconfigurable structure on the Moon. This robust system consists of truss-like robotic triangles, each formed by a continuous inflated fabric tube routed through two robotic roller units and a connecting unit. When deflated, these triangles can be compacted to roughly the volume of the roller units, offering an advantageous stowed-to-deployed volume ratio of 1 to 6.24. Upon inflation, the roller units pinch the tubes, creating corners by reducing the bending stiffness of the tube. Once fully deployed, electric motors move the robotic roller units along the tube, …

Sample Archival System For Quantum Device Manufacturing: Design, Construction, And Integration, Corbin Russ

Graduate Theses and Dissertations

In the manufacturing of quantum devices using 2-dimentional (2D) materials, current processes rely on manual operations of production. MonArk Quantum Foundry is developing a facility to semi-automatically, and ultimately automatically, produce 2D materials. One crucial step in the experimentation process is archiving these materials and completed chips for future access. The sample archival system robot was produced to fulfill this need. The system is a 4 Degree-of-freedom (DOF) cartesian storage system that stores custom aluminum chip trays of sixty samples, having a total storage capacity of 14,400 samples. The sample archive system meets key goals from: it integrates with the …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Multimodal Emotion Recognition For Human-Robot Interaction Across Neuro-Diverse Populations., Ruchik Mishra

Electronic Theses and Dissertations

This dissertation explores the integration of multimodal data streams and artificial intelligence pipelines to understand human affect in neurotypical and children with Autism Spectrum Disorder (ASD). This dissertation captures human affect in the context of human-robot interaction. For this, multiple studies have been presented with both children with ASD and neurotypical adults. This dissertation makes four contributions: 1) The first study introduces autonomy during perspective-taking teaching sessions by making verbal content generation through large language models (LLMs). This system is the first of its kind for teaching perspective-taking in a semi-autonomous manner under the supervision of domain experts. Furthermore, this …

Robot-Integrated 4d Building Information Modeling (4d Bim): Framework For Planning Safe Autonomous Construction Operations In Dynamic Environments, Hafiz Oyedimeji Oyediran

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

In the construction industry, the use of autonomous robots is considered a solution to overcome the heavy reliance on human workers to perform repetitive, strenuous, and hazardous tasks. While these robots offer the advantage of autonomous operation, ensuring their safe and efficient integration within construction sites requires precise planning. Such planning must account for the varying project complexities such as scope, site layout, tasks, timelines, existence of human workers, and other spatiotemporal conditions of the construction site. Currently, there are no methods to safely plan autonomous robot operations considering these factors within the overarching construction planning process. Thus, autonomous robots …

A Novel Robotic Pedaling Paradigm To Improve Lower Limb Movement Post-Stroke, Tom S. Ruopp

Dissertations (1934 -)

Prior work in our lab revealed that while impaired paretic neuromuscular output contributes to movement difficulties post-stroke, compensation is more related to interlimb coordination (ILC) deficits. Specifically, ILC deficits were revealed in the context of lower limb split-crank pedaling. Participants who demonstrated larger levels of compensation during a conventional, solid-crank pedaling task also demonstrated larger deficits in ILC i.e. maintaining a 180-degree phase relationship during split-crank. To address this deficit, our lab created a novel, split-crank pedaling robot named CUped. CUped (pronounced Cupid) is so called because it compels use of the paretic limb during a movement that resembles pedaling. …

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Advances Towards A Robotic Management Vehicle Suited To Nurse Row Crops To More Efficient Outcomes, Adam Mk Gronewold

Dartmouth College Ph.D Dissertations

Enhancing agricultural production while reducing input costs remains a central challenge in modern row-crop management. Recent advances in computation, imagery, and sensors are enabling more efficient practices across various agricultural domains, and automation technologies are increasingly available to manage tasks central to perennial crop development. Automation in row-crop agriculture, by contrast, lags behind. This thesis explores utilizing small, unmanned ground vehicles to transform row cropping through the implementation of unconventional, in-season management strategies. The first focus of this work considers improvements to nitrogen fertilization using small, autonomous vehicles. An agronomy experiment in corn assessed the effects of gradually applying nitrogen …

Three-Rigid-Link Manipulator Robot Inverse Dynamic Model Using Ann Based On Pid Controller, M.A. El-Gohary, M.S. Elksas, A. E. Banna, S.G. Ahmad

Journal of Engineering Research

This work proposes an inverse dynamics model of a three-rigid -link manipulator robot employing a PID controller based on artificial neural networks. In order to attain precise position, neural network control algorithms are designed to handle nonlinear issues including robot manipulator control and uncertainty compensation. An artificial neural network with two layers that uses feedforward training is trained using the back propagation technique. By just fusing the ANN with other traditional control techniques, the suggested controller gives the network more information on the composition and dynamics of the system. The PID controller generates data that is used to train neural …

Foundational Robotics, Akshit Lunia, Ananya Nagabhushana Rao, Yue Wang

Robotics

This textbook is a product of Co-DREAM OER (Collaborative Development of Robotics, Mechatronics, and Advanced Manufacturing Open Educational Resources), a US Department of Education-funded initiative to develop Open Educational Resource textbooks on robotics, mechatronics, and advanced manufacturing processes. It has been created by a diverse team of scholars and graduate students from across the country and is intended for higher-level robotics courses offered by 4-year undergraduate programs.

Virtual Fixtures For Teleoperated Robots For The Visually Impaired, Vishwaak Chandran Thamaraiselvan

Computer Science and Engineering Theses - Archive

This paper presents our preliminary study on enabling individuals with visual impairments to safely operate mobile robots and vehicles. To achieve this, we developed a teleoperation with accessibility at its core. The system incorporates features that enhance usability and situational awareness, including assistive control based on artificial potential fields to prevent collisions and ensure smooth navigation. It also provides multimodal feedback through (a) haptic vibrations on the gamepad controller, which convey the proximity of nearby objects detected by the robot’s laser sensor, and (b) color-coded overlays that differentiate paths, obstacles, and people through semantic segmentation performed by a deep neural …

3d Perception, Mapping, And Navigation For Mobile Cobot, Tuan T. Dang

Computer Science and Engineering Dissertations - Archive

Service robots are migrating from tightly controlled factory lines into offices, hospitals, and homes, where they must perceive, remember, and act amid people, clutter, and perpetual change. Humans solve this daily by forming compact, task-relevant “cognitive maps”: we sample just enough sensory detail to guide the moment, stitch those snapshots into a sparse topological scaffold, and continuously refine it as we move. Guided by that insight, this dissertation proposes a biologically inspired mapping framework that turns partial RGB-D observations into a hybrid temporal-spatial memory—locally metric for centimeter-scale navigation yet globally topological for room-to-building navigation. The system first distills raw depth …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …