Navigation, Guidance, Control, and Dynamics Commons^™

Crowd Monitoring And Firearm Detection Uas, Arjun Nambiar, Sang-A Lee, Diego Espino, Will Obot Jr, Ethan Encarnacion, Jarrett Usui, Jacob D. Kline

Discovery Day - Daytona Beach

The increasing complexity of public safety operations in urban and high-density environments necessitates intelligent, mobile surveillance systems capable of real-time threat identification and situational awareness. Traditional monitoring approaches, such as fixed CCTV systems and manual observation, are often limited by coverage, scalability, and response latency in complex environments or large-scale events. This project addresses these challenges through the development of a computer vision-enabled Uncrewed Aerial System (UAS) designed for crowd monitoring and firearm detection. The primary objective is to design and validate a modular, Artificial Intelligence (AI)-Machine Learning (ML)-driven model capable of identifying firearms within dynamic environments while supporting real-time …

Modernization Of Deicing Operations Utilizing Uncrewed Aircraft Systems, Christopher Sidor, Hunter Lisle, Zackrey Schraeder

Discovery Day - Daytona Beach

Modernization of Deicing Operations Utilizing Uncrewed Aircraft Systems   The use of Uncrewed Aircraft Systems (UAS), commonly known as drones, has become more apparent in everyday life within the United States (US). The Airport Cooperative Research Program (ACRP) has provided an opportunity to utilize drones to improve quality of life and operational efficiency at airports across the US. Students from Embry-Riddle Aeronautical University under the callsign Team Frostbite, consists of Hunter Lisle, Zakrey Schraeder, and Christopher Sidor. The team provides a potential solution utilizing UAS to measure the efficiency of deicing applications at Patrick Leahy Burlington International Airport (BTV) in Burlington, …

The Electric Crackdown: Exploring The Human Factors Impacts Of Controls & Displays In Evs Across The Market, Rae Okada, Robin Hanen, Liam Brennan

Discovery Day - Daytona Beach

Title: The Electric Crackdown: Exploring the Human Factors Impacts of Controls & Displays in Electric Vehicles Across the Market   Despite a global adoption of electric vehicles (EVs) with partial driving automation (i.e., advanced driver assistance systems; ADAS), differences exist at societal (i.e., legislation, regulation), individual (i.e., use of), and system (i.e., vehicle-to-vehicle) levels surrounding these vehicles. While these former societal divergences can be considered based on the: (1) mechanisms promoting or limiting EV adoption, (2) number of EVs on the road, and (3) respective incident reports; system differences require investigation for each vehicle based on its make and model. In …

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Adaptive – Compositional Method For Coordinating Subsystems At Different Levels In Hierarchical Control Systems, Husan Zakirovich Igamberdiyev, Uktam Farxodovich Mamirov Mr, Inomjon Ilxom Ugli Abdukaxxarov Mr

Technical science and innovation

This paper examines the problem of synthesizing algorithms for the composition of subsystems at various levels within complex hierarchical control systems under conditions of uncertainty and resource constraints. It is demonstrated that classical composition algorithms, which rely on the intersection of admissible sets and the use of local optimality criteria, suffer from several significant drawbacks. These include a combinatorial increase in complexity, dependence on the expert selection of parameters, and limited applicability in dynamic environments. To overcome these shortcomings, an adaptive-compositional method is proposed. This method is based on the dynamic recalculation of coordination parameters, the integration of model predictive …

A Comparative Study Of Model Predictive Control And The Stanley Method For Vehicle Path Tracking Applications, Noah S. Fitzgerald

Master's Theses

This thesis compares a model predictive controller (MPC) and a lateral Stanley controller for vehicle path-tracking applications under simulation-based and perception-driven operating conditions. Both controllers were evaluated in simulation using a nonlinear dynamic bicycle model executing single and double lane change maneuvers. Following simulation-based evaluation, both controllers were implemented on hardware within a perception-driven steering-control pipeline. This pipeline utilized recorded sensor data from the MXcarkit 1/8^th-scale autonomous vehicle platform, incorporating lane instance segmentation and homography-based roadway estimation.

Under idealized simulation conditions, the MPC demonstrated improved trajectory-tracking performance during aggressive maneuvers while requiring greater steering activity and computational effort …

Design Optimization Of Active-Stabilizing Canards For High-Powered Rockets, Simon Babcock

Senior Honors Theses

Rocket canards are a common means of stabilizing a high-powered rocket in flight by producing aerodynamic forces to correct the rocket's trajectory. The size, shape, and position of the canards relative to the rocket body determine their control effectiveness and aerodynamic efficiency – two objectives of canard design with an inverse relationship to each other. By integrating automated iterative rocket trajectory simulation with adaptive surrogate modelling, this research optimized the canard planform geometry for a high-powered rocket to maximize the canards’ control effectiveness and aerodynamic efficiency through multi-variable, multi-objective design optimization. The canard design was first parameterized into four design …

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 …

Optimizing Controller Speed And Torque To Reduce Drivetrain Stress, Selene J. Welch

SACAD: Scholarly Activities

This project investigates how torque delivery from the motor controller affects mechanical stress on the rear freewheel ratchet mechanism of a three-wheel electric race car. Excessive initial torque has caused accelerated wear and tear on the ratchet mechanism, reducing drivetrain reliability during Kansas ElectroRally competitions. Preliminary testing showed that slower, gradual acceleration prevented malfunction and allowed the vehicle to maintain top speed reliably. Prior research shows that torque-control strategies strongly influence electric-drive performance. Current research focuses on optimizing the controller’s torque and speed using the Alltrax Software ToolKit to reduce drivetrain tension while preserving the car’s performance capabilities.

Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina

Mathematical Modelling and Numerical Simulation with Applications

We study the problem of data transmission from mobile platforms operating in high-speed transit between cellular base stations, under conditions of unstable and intermittent connectivity. Conventional queueing and connectivity models often fail to capture the combined effects of rapidly changing signal conditions, finite buffer capacity, and dynamic topology. We aim to develop a tractable yet expressive model that integrates stochastic link availability, queue dynamics, and buffer control. We propose a mathematical framework based on queues whose service intensities are modulated by a continuous-time Markov chain (CTMC) representing signal conditions along a high-speed trajectory. The core subsystem is a two-stage (aggregation …

Scenarioxp: A Complete Scenario–Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Student Research Symposium (SRS)

Today is an age of exiting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Investigation Of The Impact Of The Shape Of The Wings On Formula E Racing Car Performance: Enhancements For Optimal Aerodynamics, Ednie Marthe Adlaikah Jozil

Honors Undergraduate Theses

The aerodynamic performance of a Formula E chassis significantly dictates its overall race efficiency, directly impacting crucial parameters such as battery range and thermal management. This thesis investigates the external aerodynamics of the baseline Gen 2 Formula E car and evaluates the performance gains of two novel aerodynamic packages: a "Fully Modified" configuration and a "Flat Rear Wing" design. Computational Fluid Dynamics (CFD) simulations were conducted to analyze drag coefficients (Cd), downforce generation, and vehicle wake structures at race-relevant free-stream velocities (e.g., 37 m/s and 89 m/s). To ensure numerical robustness, the computational setup was validated using a smooth sphere …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Multi-Level Energy Optimization For Connected And Automated Vehicles: From Cooperative Multi-Vehicle Control To Individual Powertrain Management, Pruthwiraj Santhosh

Dissertations, Master's Theses and Master's Reports

The transportation sector currently accounts for nearly 30% of global energy consumption, necessitating urgent advancements in vehicle efficiency to meet Net Zero targets. Leveraging connectivity and automation, this dissertation proposes and validates methodologies to reduce the energy consumption of light-duty vehicles at both fleet and individual levels.

First, a validation framework is developed to bridge the “simulation-to-real world” gap in Cooperative Automated Vehicle (CAV) research. Moving beyond virtual simulations, the study establishes a methodology for physically validating centralized control architectures via a custom Cellular V2X network. By synchronizing vehicle-powertrain models with physical test vehicles, the framework successfully orchestrates complex arterial …

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

Control Strategies For 6-Dof Quadcopter Uavs: Cascade Pid Stabilization In White Noise Conditions, An Vo Van, Hung Ha Duy

Makara Journal of Technology

In this study, a cascade PID control structure is proposed and implemented for a 6-degree-of-freedom (6-DOF) unmanned aerial vehicle (UAV) to enhance stability and trajectory tracking capabilities under both noise and non-noise conditions. The controller was designed based on the Tyreus–Luyben tuning method and was evaluated using quantitative metrics, including rise time, settling time, overshoot, and steady-state error. Simulation results on MATLAB/Simulink show that the controller achieves high performance in angular channels (ϕ, θ, ψ) and altitude (z) with a short rise time (< 2s), slight overshoot (< 1%), and nearly eliminated steady-state error. However, the horizontal position channels (x, y) have a longer settling time (~110s) and are sensitive to white noise. Quantitative comparisons with other control methods show that the cascade PID outperforms the standard PID in terms of accuracy and stability, achieving a performance comparable to LQR under noise-free conditions, but is less robust in the presence of noise than advanced methods like SMC and MPC. These results confirm the feasibility of cascade PID in UAV applications and indicate potential future improvements by integrating nonlinear, adaptive, or intelligent control strategies.

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Resilient Control Framework For Ev Motor Drive System Subject To Cyber-Physical Security, Ali Arsalan

All Dissertations

The electric drive system (EDS) in electric vehicles (EVs) is one of the key safety-critical components. As IoT-enabled communication infrastructure for modern cyber-physical automotive systems continues to evolve, the importance of securing EDS against cyber threats along with physical faults, has become increasingly prominent. Among physical faults, power switches are particularly vulnerable and exhibit the highest susceptibility to open-circuit faults (OCFs). A compromised EDS, whether due to cyber threats or physical issues, can lead to excessive mechanical vibrations, increased thermal stress, fluctuations in electromagnetic torque, and elevated total harmonic distortion. These factors can substantially undermine traction control stability and jeopardize …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Decision Field Theory For Human-Multi-Robot Collaboration: Human-Centric Decision-Making For Multi-Robot Systems, Ryan Mbagna Nanko

All Theses

At first glance, choosing between an apple and an orange appears to be a straightforward matter of personal taste; however, this seemingly simple preference opens a window into the multifaceted world of decision-making, revealing the complex interplay of cognitive processes, psychological, and behavioral-economic principles that guide our choices \cite{bandyopadhyayRoleAffectDecision2013}. By unpacking these nuanced perspectives, we uncover insights that can drive more effective human-robot interaction and collaboration.

Modeling human cognition requires understanding the evolution of choice utility and the influence of emotions. Decision Field Theory (DFT) stands out by capturing the fluctuating nature in human preferences over time, explaining why choices …

Enhancing Autonomous Vehicle Resilience Through Engineering Requirements And Snow-Adaptive Lane Detection, Alexandra Marie Masterson

Masters Theses

This thesis investigates two distinct but interrelated challenges in the development of resilient autonomous vehicle (AV) systems: the formalization of engineering requirements for AV perception subsystems and the enhancement of visual lane detection under snow-covered road conditions. In the first study, field experiments were conducted using a campus-deployed autonomous research vehicle to evaluate the impacts of perception related failures including GPS outages, HD map inconsistencies, and weather interference—on vehicle operation. These findings were used to develop a set of qualitative engineering requirements that promote AV resilience through proactive design. In the second study, a custom snow-focused lane detection dataset was …

Effect Of Using Machine Learning To Improve Active Suspension Control For Vehicle Stability, David A. Butz

Masters Theses

This study demonstrates that it is possible to use road surface classification as a means of informing active suspension systems in order to limit their activity. An approach was taken to improve the response of an active suspension control system by classifying road surfaces in near real time. A control system model was developed to represent a full-body vehicle, and an AI was used to analyze road vibration noise. The model was adapted to allow the AI to select from multiple control signals based on the AI’s analysis of road vibration noise. The objective of the study was to demonstrate …

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 …

Training Safety Control Filters Using High-Dimensional And Un-Labeled Data, Yuxuan Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Synthesizing control policies that preserve the safety of autonomous systems is a challenge that remains to be solved. Towards that goal, control barrier functions (CBFs) have been developed as mathematical constructs that can be used in real-time to correct safety-violating nominal actions to ones which preserve the safety of control systems. However, synthesizing CBFs using correct-by-construction methods has not been scalable. Instead, recent research has proposed data-driven approaches for learning CBFs in the form of neural networks. Two main challenges face such approaches: (1) labeling states as unsafe or safe ones requires the knowledge of the states in the backward …

The Impact Of System Transparency On Perceived System Reliability, Perceived System Usability, And Information Clarity In Self-Driving Car Systems, Uditkumar Nair

Theses and Dissertations

In human-computer interaction (HCI), the development of autonomous vehicle (AV) technology has created new difficulties, especially in building user confidence as well as understanding of system functioning. The effect of system transparency on user- centered outcomes, such as perceived usability, perceived system reliability, and information clarity, is examined in this thesis. In order to evaluate their experiences in both ordinary and high-stakes driving situations, participants engaged with both system- transparent user interfaces (TUIs) and non-transparent user interfaces (NTUIs) across a number of experimental scenarios. In order to assess how well each interface conveyed system logic and actions, the study included …

Analyzing The Resilience Of Infrastructure-Based Vs. Camera-Based Lane Detection In Autonomous Vehicles, Pritesh Yashaswi Patil

Masters Theses

Traditional autonomous vehicle perception subsystems that use on-board sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high-quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. This need is addressed by evaluating …