Computer Engineering Commons^™

Adaptive Multi-Sensor Fusion For Robust Autonomous Perception In Unstructured Environments, Samantha S. Carley

Theses and Dissertations

Autonomous vehicles commonly employ multiple sensors to perceive their surroundings. Coupling these sensors would ideally improve perception compared to using a single sensor. An autonomous system can be equipped with object localization and classification, often performed using a visual camera to understand a scene intelligently. Object detection and classification can also be applied to LiDAR and infrared (IR) sensors to further enhance scene awareness of the autonomous system. Herein, sensor-level, decision-level, and feature-level fusion are explored to assess their impact on perception and mitigate sensor disagreements. Specifically, the fusing of RGB, LiDAR, and IR sensor data to improve object classification …

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 …

Algorithms & Design Behind Autonomous Uavs And Ugvs Coordinated System, Aashish Dhakal

Honors Theses

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), when coordinated effectively, offer substantial potential for automating large-scale tasks—from search and rescue operations to precision agriculture. However, synchronizing these autonomous systems remains challenging, especially in time-sensitive missions requiring precision. This thesis investigates the design and algorithmic coordination of autonomous UAVs and UGVs, examining both single-vehicle scenarios and multi-agent (swarming) approaches. Using the Robot Operating System (ROS) as a communication backbone, I integrate GPS positioning with computer vision techniques through OpenCV, enabling accurate localization and object detection. During the development phase, I validate my methods using ArduPilot Software-in-the-Loop (SITL) simulations within …

Ecodrone: Autonomous Environmental Monitoring, Belsen Lee

Student Scholar Symposium Abstracts and Posters

This project presents EcoDrone, an autonomous aerial drone designed for continuous and automated environmental monitoring. Current environmental monitoring methods rely on stationary sensors or manual data collection, limiting real-time response capabilities. This reliance leads to delayed, incomplete, and spatially limited data and restricts the ability to capture real-time changes. Another challenge includes the difficulty of environmental monitoring in challenging terrain, whether it be wildfire areas, dense forestry, or mountainous terrain. EcoDrone overcomes these challenges by autonomously navigating difficult terrain to collect real-time data, offering more flexible and timely monitoring than stationary or manual methods. The central research question investigates integrating …

Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao

Dartmouth College Ph.D Dissertations

The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and …

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 …

Autonomous Search And Rescue: Real-Time Drone And Robotic Dog Integration, Robert Alexander

Electrical Engineering and Computer Science (MS) Theses

Common robotic navigation techniques often utilize GPS to set up the robot’s reference frame, which is not possible in environments, such as indoor facilities, underground passages, and disaster zones, where GPS is not available. This research explores the integration of a Boston Dynamics Spot robot with a Tello drone to form a non-GPS-based autonomous navigation system. By leveraging coordinate transformation logic, this study enables real-time aerial reconnaissance and ground-based waypoint navigation without reliance on GPS. The methodology includes software development using the Spot SDK and Tello APIs, a virtual networked solution for integration, and an experimental setup to validate navigation …

Ai-Powered Inspection: A Computer Vision System For Efficient Defects Detection In Underground Infrastructures, Rasha Alshawi

LSU New Orleans Theses and Dissertations

Undetected defects in culverts and sewer pipes pose significant risks to public safety, leading to infrastructure collapses, flooding, and transportation disruptions. Traditional manual inspections are time-consuming, costly, and prone to human error, while existing automated methods struggle with occlusions, irregular defect shapes, class imbalances, and high computational demands. To address these challenges, this dissertation develops advanced semantic segmentation systems that automate defect detection, significantly improving efficiency and accuracy.

This research introduces a series of innovative models designed to overcome these challenges in underground infrastructure inspection. Using dual-attentive mechanisms, sparsely connected blocks, and depth-separable convolutions, these models improve segmentation performance and …

Graph Based Deep Reinforcement Learning Aided By Transformers For Multi-Agent Cooperation, Michael S. Elrod

All Theses

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveil- lance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particu- larly when prior information is not available. To address these challenges, I propose an innovative framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task ex- ecution. My approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient …

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 …

Low Cost Additive Manufacturing Of Segmented Stator Composite Polymer Permanent Magnet Dc Motors, Ben Goldberg, Jordan Bailey, Connor Hawkins, Colin Haskins, Razvan Voicu

Symposium of Student Scholars

This study presents a novel approach to the design, manufacture, and optimization of segmented stators for composite construction axial flux permanent magnet DC motors. Traditional axial flux stator manufacturing is both challenging and expensive, creating a bottleneck in rapid prototyping and innovation. To overcome these limitations, the stator is divided into individually fabricated segments using advanced composite polymer materials and low-cost additive manufacturing techniques. This segmentation not only drastically reduces production complexity and cost but also allows for customized coil geometries that maximize the surface area for improved heat dissipation.

A key innovation of our design is the integration of …

Design Of A Teleoperabletendon-Driven Robotic Hand, Zachary Giese, Joseph Gober, Samuel Grisham, Avery Mahan, Aspen White, Brayden May, Aspen White

ATU Scholars Symposium

This work presents the design, implementation, and validation of a 3D-printed tendon-driven robotic hand with a bi-directional wireless control system. The robotic hand emulates human anatomical principles, focusing on replicating flexion and extension mechanisms of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints via a tendon-pulley system. Iterative prototyping resolved initial challenges in joint stiffness, tendon routing, and servo interference, culminating in a design where a single servo drives both flexion and extension for each joint using a bidirectional tendon control mechanism. A reliable bidirectional wireless communication framework, using Arduino Nano Every microcontrollers and NRF24L01 modules, enables …

Mobile Robots And Autonomous Vehicle Control: A Comprehensive Review Of The Advancements And Challenges, Elvis Tamakloe, Benjamin Kommey, Ernest Ofosu Addo, Daniel Opoku

Makara Journal of Technology

Since their inception, mobile robots have enormously changed the landscape of robotics engineering in recent years. Imperatively, the impact of mobile robots has positively transformed many sectors of human endeavors, i.e., complemented and substituted humans in areas where human interactions were difficult, hazardous, and impossible to thrive and operate. In this regard, the contributions of mobile robots to scientific, social, and economic growth, development, and advancement cannot be overlooked, especially through its decades of transition from Industry 3.0 to 4.0 over the years. To achieve maximum benefits from the use of mobile robots across all important facets, their advancements and …

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 …

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Autonomous Underwater Vehicle Planning Using Hybrid D* Lite With Ppo And Td3: Experimental Design And Performance Analysis, Matthew J. Rice

Undergraduate Theses

Autonomous Underwater Vehicles (AUVs) face significant challenges in underwater navigation, including generating smooth paths, avoiding obstacles, and adapting to complex conditions. This paper introduces a hybrid path-planning algorithm, D-RL*, that integrates the D* Lite algorithm for efficient initial pathfinding with Deep Reinforcement Learning methods to refine paths for smoother trajectories. The proposed approach addresses D* Lite's inability to produce continuous, smooth paths and baseline Reinforcement Learnings’ failures in environments requiring significant detours. Experimental results in four progressively complex environments highlight D-RL*’s ability to plan smoother paths than D* Lite while training in a shorter amount of time and generating shorter …

Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Integrating Neural Networks For Predictive Torque Control And Obstacle Avoidance In Autonomous Robot, Viswanath Kodali, Harsha Vardhan Borra, Kiran P

Northeast Journal of Complex Systems (NEJCS)

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control.

To address the challenge of obstacle avoidance in …

Metaheuristic Techniques To Optimize Trajectory Planning Of Uav Swarms: Enhancing Data Acquisition In Wireless Sensor Networks, Nada Ali Mohamed Ahmed Ahmed

Theses and Dissertations

Unmanned aerial vehicles (UAVs) have become increasingly integrated into various applications due to their cost-efficiency, rapid deployment, flexible maneuvers, and enhanced performance. This has led to the development of a new field called UAV-assisted Wireless Sensor Networks (U-WSNs), which focus on data routing, network performance optimization, and planning UAV trajectories between sensor nodes in wireless sensor networks. In this thesis, a new framework has been proposed to manage a swarm of UAVs cooperatively serving large-scale wireless sensor networks. The framework consists of three optimization problems: distributing sensor nodes among UAVs, finding optimal trajectories in the presence of obstacles, and performing …

Navigating The Future Advancing Autonomous Vehicles Through Robust Target Recognition And Real-Time Avoidance, Mohammed Ahmed Mohammed Hussein

Theses and Dissertations

The problem being tackled by this thesis is a very important one and very relevant to our days and times: it is about making improved target recognition and enhanced real-time response skills in AVs under simulated conditions. Our plan is to put some enhanced sensory capabilities into these vehicles and see if that makes them safer and more reliable. We are using as our base a particular object recognition algorithm (YOLOv7) and a particular simulation environment (CARLA). We utilized the CARLA 0.9.14 simulator on Ubuntu 20.04 as a more stable option than the initially used CARLA 0.9.15 on Ubuntu 22.04, …

Advancing Prosthetic Technology: 3d-Printed Robotic Arm With Micro Linear Actuators, Vu Tran, Nathan Reed, Mahdi Yazdanpour

Posters-at-the-Capitol

The increasing demand for affordable and accessible prosthetic solutions has driven innovation in the field of robotics. In this project, we present the design and development of a 3D-printed robotic prosthetic arm aimed at addressing the challenges faced by individuals with upper limb amputations. Our prosthetic arm utilizes five micro linear actuators to achieve precise and naturalistic movement. The design of the prosthetic arm prioritizes affordability and accessibility, with a focus on leveraging 3D printing technology to reduce manufacturing costs and enable customization. The use of micro linear actuators offers advantages in terms of compactness, lightweight construction, and efficient power …

Very Large Scale Robotics Path Planning With Centroidal Voronoi Tessellation, Xu (James) Gao

Theses, Dissertations and Capstones

Swarm robotics, also referred to as very large-scale robotics (VLSR), has emerged as a transformative approach for addressing complex tasks that are infeasible for single-robot systems. Applications range from environmental monitoring and disaster response to large-scale agricultural and industrial operations. However, as the number of robots in a swarm increases, so do the challenges associated with motion control, energy efficiency, and scalability. These challenges necessitate innovative solutions that balance microscopic robot behaviors with macroscopic system-level objectives.

In this thesis, we address these challenges by building upon existing research [40], which introduced novel methods for optimizing swarm robotics systems using macroscopic …

Ai In Higher Ed, Where Are We Now?: Insights From The 2025 Educause Ai Landscape Study, Angela Neria, Jeff Burns

Posters

Curious about how higher education is really using AI? Wondering what’s next for AI policies, workforce impacts, and leadership strategies? The 2025 EDUCAUSE AI Landscape Study has the answers! Based on fresh data from institutions across higher ed, this study highlights key trends, challenges, and opportunities in AI adoption. Stop by our poster session to get a quick snapshot of where AI stands today—and where it’s headed. Let’s talk about what these findings mean for PSU and the future of AI in higher education!

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 …

Exploring Instruction Generation For Uavs: Dataset Adaptation, Model Behavior, And Diagnostic Insights, Seyedarman Vaziri Bozorg

Computer Science and Engineering Theses - Archive

This thesis explores the development of an answering agent capable of generating natural language instructions for unmanned aerial vehicles (UAVs), grounded in a limited, real-world dialogue dataset. The objective is to adapt a static dataset into a training pipeline that can support instruction generation and serve as a foundation for future interactive systems involving question-asking agents and internal dialogue. A hybrid architecture is implemented using a semantic teacher model (MPNet) and a T5-base encoder-decoder trained with contrastive and supervised objectives. The adapted training process yields statistically acceptable performance across standard evaluation metrics. However, qualitative analysis reveals a mismatch between metric …

Transformer And Recurrent Architectures For Dynamics Prediction And Policy Learning On Long-Horizon Tasks, Vinal Jitendrabhai Gadhiya

Computer Science and Engineering Theses - Archive

Model-based reinforcement learning promises improved sample efficiency by learning environment dynamics and using them for planning or policy improvement. However, the choice of neural architecture for dynamics prediction significantly impacts the model's ability to capture temporal dependencies and maintain long-term context, capabilities crucial for complex, open-world environments.

This thesis investigates three neural architectures for learning world models: Transformer-based, GRU-based, and a hybrid Transformer+GRU approach. We evaluate these architectures on Crafter, a 2D open-world survival environment that requires long-horizon planning and sequential task completion. In Crafter, agents must perform hierarchical sequences of actions, such as collecting wood, placing a table, and …