Robotics Commons^™

En-Feat: An Effective Feature Selection Method Using Ensemble Approach, Sasank Nath, Dhruba Kumar Bhattacharyya

Mansoura Engineering Journal

Feature selection is a crucial step in machine learning and data preprocessing, significantly influencing model performance and interpretability. This paper presents a comprehensive study and contributions in the domain of feature selection by integrating traditional learning techniques with ensemble-based, proposing an effective approach. We propose a Mutual Information-based feature aggregation approach applied to union sets of features, aiming to derive an optimal subset of features that maximizes accuracy. Then, we employ an ensemble method that utilizes forward selection over union sets to identify the optimal feature subsets through sequential feature selection. Our ensemble-based feature selection method called En-feat, is evaluated …

Sam-1: An Assistive Robot For Remote Patient Monitoring, Aana Farhana Ahsan

USF Tampa Graduate Theses and Dissertations

The global aging population has increased the demand for caregiving, while the availability of caregivers has not grown at the same pace. These trends have contributed to disparities in consistent and accessible care for older adults and increased caregiver burden. Geriatric care robots have emerged as a promising solution to support older adults and reduce caregiver burden. Existing systems have been developed for a wide range of applications, including rehabilitation, social support, assistance with activities of daily living, therapy, and caregiver telepresence. This thesis presents the development of SAM-1, an assistive robot designed to support remote monitoring of older adults …

Robust Real-Time Uav Target Tracking With Onboard Vision-Based Yaw Control, Rylan Malarchick, Jose Castelblanco, Enrique Amaya, Carmen Dimario, Graysen Brinkman, Chirag Kumar, Kiwon Yoon, Sajid Berhane

Beyond: Undergraduate Research Journal

Autonomous tracking of agile unmanned aerial vehicles (UAVs) presents significant challenges for real-time perception and control systems. This work presents AIRHOUND (Autonomous Intelligent Rotorcraft for Hostile Object Unified Navigation and Detection), a UAV platform implementing vision-based yaw tracking through a modular ROS2 software architecture. The system employs YOLOv8 object detection optimized with NVIDIA TensorRT for embedded deployment on an NVIDIA Jetson Orin companion computer. Detected targets are processed through a geometric tracking module that converts pixel coordinates to angular yaw errors using pinhole camera intrinsics, with a proportional controller generating rate-limited yaw commands. These commands are streamed to a PX4 …

Vision-Guided Motion Planning For Autonomous Catch With A Robotic Arm, Kayla Go-Oco, Ashik K. Islam, Raegan Gritzmacher

Electrical Engineering

Modern robotic systems are often evaluated using static and highly controlled tasks, such as pick-and-place demonstrations, which do not fully represent the uncertainty and adaptability required in real-world environments. A major challenge in robotics research is enabling robotic systems to perceive, track, and respond to dynamic objects in real time while maintaining accurate and reliable motion control. This project addresses these challenges by developing an autonomous robotic platform in which an OpenMANIPULATOR-Y robotic arm detects, tracks, and attempts to catch a moving tennis ball using a vision-guided control system. The system integrates YOLO-based object detection with an Intel RealSense D455 …

Autonomous Vision-Based Litter Collection Rover, Dante Michael Benedetti, Benjamin Scott Tavares, Nathan Heil

Electrical Engineering

This report documents the design, implementation, and testing of an autonomous litter-collection rover developed as a Senior Project Design Lab (EE 460/463/464) at California Polytechnic State University. The rover integrates autonomy, computer vision, embedded real-time control, mecanum-wheel omnidirectional mobility, and a two-degree-of-freedom robotic arm to detect, approach, and collect small ground-level litter such as bottles, wrappers, and paper fragments.

The system uses a two-layer compute architecture: an NVIDIA Jetson Orin Nano running ROS 2 for perception, SLAM, and path planning, paired with an STM32L4A6ZG microcontroller for real-time motor control and odometry. The robot is built on a multi-level aluminum frame …

Integration Of Robotic Arm And Conveyor With Programmable Logic Controller, Drake A. Small

Master's Theses

To meet new curriculum demands brought on by Cal Poly's upcoming switch to semesters, a new, cross-disciplinary lab module was developed for EE 435 (Industrial Power Control and Automation). The module emphasizes career-applicable skills, preparing students for the field of controls engineering within the manufacturing industry. These skills include robotic control, computer networking and configuration, and embedded systems. The work focused on integrating a collaborative robot arm to pick-and-place boxes on a conveyor. A myCobot 320 Pi and an Ultimation Powered Roller MDR Conveyor were integrated with the existing PLC system using Modbus RTU and EtherNet/IP communication protocols, respectively. A …

Design And Parametric Study Of A Mems-Based Reservoir Computer For Reinforcement Learning, Andrew P. Carr

Master's Theses

Single-node reservoir computing (RC) is a hardware-efficient approach to machine learning, leveraging the dynamics of physical systems. In this work, two reinforcement learning algorithms, Q-learning and Proximal Policy Optimization (PPO), are applied to a simulated micro-electro-mechanical system (MEMS)-based reservoir computer to solve both discrete and continuous control tasks. MEMS-based reservoirs are low-power, compact, and their natural frequencies (kHz to MHz) pair well with real-time control loops. To explore the relationship between reservoir dynamics and learning performance, a parametric study is conducted on two reservoir hyperparameters, reservoir size and neuron separation, using CartPole-v1 and MountainCar-v0. The RC successfully learns multiple tasks …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Simulated Intelligence, Surveillance, And Play, Atienah Cape

Theses - ALL

This MFA thesis explores modern panopticism and the feeling of being watched by creating and installing Four Real Friends, an interactive robotic artwork inspired by the 1998 Furby. The project looks at how surveillance technologies are not just outside forces, but part of daily life, often feeling familiar, playful, and even social. The installation uses hardware design, custom circuits, computer vision, and real-time data display to collect and process how visitors interact in the gallery. Its nostalgic and friendly look encourages people to take part, while still showing how data is collected and displayed as it happens. This makes surveillance …

Symmetries Of Legged Locomotion: Theory And Applications In Animals And Robots, Jiayu Ding

Dissertations - ALL

Legged locomotion in animals and robots is naturally modeled as a hybrid dynamical system: smooth body evolution is repeatedly interrupted by discrete contact events such as touchdown and liftoff. Although symmetry is a powerful organizing principle in smooth dynamical systems, a comparably systematic symmetry framework for hybrid legged locomotion remains underdeveloped. This dissertation develops such a framework for a scoped class of periodic hybrid locomotion models and uses it to relate theoretical gait analysis, animal locomotion studies, and robotic gait generation. The dissertation first extends symmetry notions from autonomous dynamics to hybrid legged systems and distinguishes symmetries of the hybrid …

Designing A Collaborative Uav-Ugv System For Autonomous Challenge Completion, Ember Reysen

Honors Theses

The coordination between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) holds significant potential for completing complex tasks such as disaster response and construction surveillance by leveraging the strengths of aerial flight and stable on-ground transportation. Unfortunately, coordination between UAVs and UGVs is extremely complex and task-dependent, leading to limited understanding of autonomous collaboration. This thesis explores the process of designing an UAV-UGV collaborative system capable of aerially scouting a target destination, relaying its coordinates to a ground vehicle, and performing a precision landing for unison travel. I utilize computer vision techniques via OpenCV for target marker identification, ROS …

Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez

LSU New Orleans Theses and Dissertations

Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …

Stretch Web Teleoperation Keyboard Final Report, Aaron Chou, Francisco Irazaba

Computer Engineering

This project added keyboard controls to the Hello Robot Stretch Web Teleop interface. The goal was to make the robot easier to control from a web browser without needing a gamepad or separate terminal-based keyboard program. The new interface allows users to control different parts of the robot with customizable key bindings.

Learning Adaptive Control For Safe Collaborative Autonomous Driving, Fabian Alexis Hernandez

Theses and Dissertations

Connected autonomous vehicles improve urban driving through collaborative perception via Vehicle-to-Everything (V2X) communication. Frameworks such as V2Xverse leverage this collaboration for planning and perception, yet their controllers rely on fixed parameters that cannot adapt to varying traffic. Control Barrier Functions (CBFs) enforce safety by constraining actions within a safe set, but a fixed barrier gain imposes a single operating point: conservative settings reduce throughput while permissive settings under-react to hazards.

This research proposes an adaptive CBF framework that learns state-dependent, class-specific barrier gains via constrained Reinforcement Learning (RL). A compact policy outputs separate gains for vehicles, pedestrians, and bicycles, parameterizing …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Temporal Logic Planning In Semantic Maps Of Unknown Environments Using Tl-Rrt, Dongrui Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Autonomous mobile robots are increasingly expected to perform complex missions in unstructured environments. Traditional path planning approaches handle simple point-to-point navigation, but struggle with complex tasks that involve temporal and logical orderings of objectives. Linear Temporal Logic (LTL) provides a method for complex missions (e.g., sequential visits to multiple targets or surveillance tasks) in a strict way. This thesis presents an integrated planning framework that enables a robot to satisfy LTL-based task specifications in an unknown environment by combining a Temporal Logic RRT* (TL-RRT*) planner with semantic mapping. The robot builds a semantic map of its environment online using simultaneous …

A Low-Cost Motion Classification System For A Stuffed Animal Using An Imu And Machine Learning, Rachel N. Guynes

Honors Theses

One of the many fields that has seen the integration of robots is therapy. Zoomorphic robots (ZR) are designed to look and behave like animals to assist in Animal Assisted Therapy (AAT) practices. Studies show that ZRs can provide benefits similar to working with an actual animal; however, their high cost limits their accessibility. This thesis documents the process of building a real-time, low-cost motion classification system that can be attached to a stuffed animal to make it more interactive. Using a Random Forest (RF) classifier, the system identifies movements with approximately 81.67% accuracy.

Approach To Physics Education Using Local Ai With Rag For Open Educational Materials Generation, Dmitriy Beznosko

All Things Open

The new tool that starts to enter all parts of our life is AI – and it enters education as well. There are two standing large concerns with using AI for education – the safety of students’ data and the AI missing specific knowledge about the given class. The approach of using the Retrieval Augmented Generation (RAG) provides the user data to the locally run LLM model (using Ollama framework, a free and open-source tool that allows you to run large language models locally on your system) as a context for the generation of the OER materials. As this AI …

Multiple-Valued Quantum Automata For Robotics, Yuchen Huang

Dissertations and Theses

This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches.

The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce …