Navigation, Guidance, Control and Dynamics Commons^™

Improving Multi-Agent Swarm Collision Avoidance Using Reciprocal Velocity Obstacles, Nick Wilson

Discovery Day - Daytona Beach

As multi-agent systems grow increasingly complex, physical robotic swarms are essential to bridge the gap between limited software simulations and real-world application. The BID4R STARS swarm provides a physical platform for testing diverse algorithms, yet its success relies heavily on fundamental agent capabilities—most notably, obstacle avoidance. Preventing intra-swarm collisions is critical to avoid hardware damage and experimental disruption. To address this challenge, this project implements the Reciprocal Velocity Obstacles (RVO) algorithm onto the STARS swarm. While standard collision avoidance algorithms often overcorrect and induce oscillatory agent movement, RVO factors in the velocity and anticipated responses of all agents involved in …

Nano Aircraft Using Underactuated Technology (Naut), Giol Vinyals I Roca

Discovery Day - Daytona Beach

The Nano Aircraft using Underactuated Technology (NAUT) project presents a novel swashplateless rotor system that achieves cyclic pitch control using a single actuator, eliminating the need for conventional multi-servo swashplate assemblies. This approach is specifically tailored for nano unmanned aircraft systems (UAS) operating within Advanced Air Mobility (AAM) environments, where size, weight, and power constraints demand highly efficient and simplified control architectures. A functional prototype has been developed and experimentally validated, demonstrating measurable cyclic pitch variation and effective control authority. Multiple mechanism variants are currently being investigated to evaluate performance trade-offs in responsiveness, mechanical efficiency, and aerodynamic effectiveness. These configurations …

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 …

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 …

Orbital Maneuvers And Interplanetary Trajectory Design Via Reinforcement Learning, Roberto Cuéllar Rangel

Doctoral Dissertations and Master's Theses

This dissertation investigates the application of reinforcement learning (RL) to the design and optimization of low-thrust spacecraft trajectories, with an emphasis on autonomy, adaptability, and robustness in the presence of system uncertainties and unmodeled perturbations. Classical approaches to low-thrust trajectory design are predominantly grounded in optimal control theory, which relies on the availability of precise dynamical models and often requires problem-specific reformulation and solver tuning. While optimal control methods offer high accuracy under deterministic conditions, their sensitivity to stochastic disturbances and computational limitations in highly nonlinear or uncertain environments pose significant challenges for future autonomous space missions.

To address these …

Real-Time Fiducial Marker Based Localization For Autonomous Unmanned Aerial Vehicle Navigation, Sourav Raxit

LSU New Orleans Theses and Dissertations

By harnessing fiducial markers as visual landmarks in the environment, Unmanned Aerial Vehicles (UAVs) can rapidly build precise maps and navigate spaces safely and efficiently, unlocking their potential for fluent collaboration and coexistence with humans. Existing fiducial marker methods rely on handcrafted feature extraction, which sacrifices accuracy. On the other hand, some deep learning pipelines for marker detection fail to meet real-time runtime constraints crucial for navigation applications. In this work, I propose YoloTag- a real-time fiducial marker-based localization system. YoloTag uses a lightweight YOLO v8 object detector to accurately detect fiducial markers in images while meeting the runtime constraints …

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.

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 …

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

Theses and Dissertations

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

Koopman-Based Modeling For Nonlinear Control Of Multirotor Uavs, Simone Martini

Electronic Theses and Dissertations

This PhD dissertation focuses on adopting the emerging Koopman Operator theory for modeling and nonlinear control of multirotor UAVs, focusing specifically on quadrotors for proof-of-concept demonstration purposes.

The Koopman Operator theory is based on the foundation that nonlinear dynamics in the state space may be represented as a linear evolution of some functions in the state space. Thus, using appropriately defined and possibly nonlinear functions of the state variables, called observables, as a new and maybe infinite set of coordinates that are referred to as lifted space, the original nonlinear dynamics appear to be linear. The implications of this theory …

Implementation Of Path Planning Methods To Detect And Avoid Gps Signal Degradation In Urban Environments, Ayush Raminedi

Doctoral Dissertations and Master's Theses

In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation …

State Omniscience For Cooperative Local Catalog Maintenance Of Close Proximity Satellite Systems, Chris Hays

Doctoral Dissertations and Master's Theses

Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local …

Nonlinear Guidance And Control Of Unmanned Aerial Manipulators For Delivering A Payload On A Moving Platform, Ravi Gyawali

Mechanical and Aerospace Engineering Theses - Archive

Unmanned Aerial manipulators (UAMs) are a class of Unmanned Aerial Vehicles (UAVs) equipped with a manipulator. By combining the aerial mobility of a UAV with a manipulator's dexterity, these hybrid systems can perform a wide range of complex tasks while reducing risks and costs. As a result, they are increasingly being utilized for military, industrial, and agricultural applications.

The thesis presents a novel approach for a multi-UAM system to collaboratively deliver a payload on a stationary or maneuvering platform. A sliding-mode-based guidance law, sourced from existing literature, is integrated with a combined control technique for the UAVs and their respective …

On Uncertainty For Ill-Posed Robot Decision Problems, Jared Joseph Beard

Graduate Theses, Dissertations, and Problem Reports (ETD)

As robots adopt more real world responsibilities, they will be expected to solve more complicated problems. In some cases limited prior knowledge will result in unmodelled environmental conditions; in others, multiple users may have competing perspectives on how to frame a decision problem. Many existing frameworks, namely Markov decision processes (MDP) presuppose users have identified a specific problem with models sufficient to solve or learn a problem. If we wish to extend MDPs to novel problems or those heavily dependent on user feedback, autonomous decision makers must be able to identify limitations in how a given problem is framed and …

External Runtime Execution Monitoring Of A Cyber Physical System Via Trace Interfaces, Peter Vaughan Truslow

Theses and Dissertations

In the past two decades, Unmanned Aerial Systems have progressed from expensive military hardware or one-off custom builds, to include off-the-shelf drones that can be purchased for a rather affordable price and flown by nearly anyone. As the technology and performance have improved, the door is opened to applications that require operation in environments where the consequences for failure are high, such as operating in the navigable airspace or in urban environments, or with human passengers. This requires a great deal of trust in the reliability and integrity of the control systems of the aircraft. A method of monitoring the …

Six-Degree-Of-Freedom Optimal Feedback Control Of Pinpoint Landing Using Deep Neural Networks, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. …

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

Accurate Indoor Navigation System Based On Imu/Lp-Mm Integrated Method Using Kalman Filter Algorithm, Abdullah Mohammed Bahasan

Hadhramout University Journal of Natural & Applied Sciences

Abstract

The demand for navigation systems is rapidly increasing, especially in indoor environments which the signal of GPS is not available. Therefore the Inertial Measurement Unit (IMU) system is a suitable navigation system in such indoor environments. It usually consists of three accelerometers and three gyroscopes to determine position, velocity and attitude information, respectively, without need of any external source. But this type of navigation systems has errors growth with time due to accelerometers and gyroscopes drifts. This paper introduces indoor navigation system based on integrated IMU navigation system with proposed system called Landmarks Points-Map Matching (LP-MM) system using Kalman …

Motion Planning In Artificial And Natural Vector Fields, Bernardo Martinez Rocamora Junior

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation advances the field of autonomous vehicle motion planning in various challenging environments, ranging from flows and planetary atmospheres to cluttered real-world scenarios. By addressing the challenge of navigating environmental flows, this work introduces the Flow-Aware Fast Marching Tree algorithm (FlowFMT*). This algorithm optimizes motion planning for unmanned vehicles, such as UAVs and AUVs, navigating in tridimensional static flows. By considering reachability constraints caused by vehicle and flow dynamics, flow-aware neighborhood sets are found and used to reduce the number of calls to the cost function. The method computes feasible and optimal trajectories from start to goal in challenging …

Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer

Symposium of Student Scholars

When navigating in unknown and unstructured environments, Unmanned Arial Vehicles (UAVs) can struggle when attempting to preform Simultaneous Localization and Mapping (SLAM) operations. Particularly challenging circumstance arise when an UAV may need to land or otherwise navigate through treacherous environments. As the primary UAV may be too large and unwieldly to safely investigate in these types of situations, this research effort proposes the use of actively guided CanSats for assisting in localization and mapping of unstructured environments. A complex UAV could carry multiple of these SLAM capable CanSats, and when additional mapping and localization capabilities where required, the CanSat would …

A Brief Literature Review For Machine Learning In Autonomous Robotic Navigation, Jake Biddy, Jeremy Evert

Student Research

Machine learning is becoming very popular in many technological aspects worldwide, including robotic applications. One of the unique aspects of using machine learning in robotics is that it no longer requires the user to program every situation. The robotic application will be able to learn and adapt from its mistakes. In most situations, robotics using machine learning is designed to fulfill a task better than a human could, and with the machine learning aspect, it can function at the highest level of efficiency and quality. However, creating a machine learning program requires extensive coding and programming knowledge that can be …

Active Localization For Robotic Systems: Algorithms And Cost Metrics, Jared Strader

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the real world, a robotic system must operate in the presence of motion and sensing uncertainty. This is caused by the fact that the motion of a robotic system is stochastic due to disturbances from the environment, and the states are only partially observable due noise in the sensor measurements. As a result, the true state of a robotic system is unknown, and estimation techniques must be used to infer the states from the belief, which is the probability distribution over all possible states. Accordingly, a robotic system must be capable of reasoning about the quality of the belief …

Localization Algorithms For Gnss-Denied And Challenging Environments, Chizhao Yang

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as "fingerprint" matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the …

Planning Algorithms Under Uncertainty For A Team Of A Uav And A Ugv For Underground Exploration, Matteo De Petrillo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Robots’ autonomy has been studied for decades in different environments, but only recently, thanks to the advance in technology and interests, robots for underground exploration gained more attention. Due to the many challenges that any robot must face in such harsh environments, this remains an challenging and complex problem to solve.

As technology became cheaper and more accessible, the use of robots for underground ex- ploration increased. One of the main challenges is concerned with robot localization, which is not easily provided by any Global Navigation Services System (GNSS). Many developments have been achieved for indoor mobile ground robots, making …

Planetary Rover Inertial Navigation Applications: Pseudo Measurements And Wheel Terrain Interactions, Cagri Kilic

Graduate Theses, Dissertations, and Problem Reports (ETD)

Accurate localization is a critical component of any robotic system. During planetary missions, these systems are often limited by energy sources and slow spacecraft computers. Using proprioceptive localization (e.g., using an inertial measurement unit and wheel encoders) without external aiding is insufficient for accurate localization. This is mainly due to the integrated and unbounded errors of the inertial navigation solutions and the drifted position information from wheel encoders caused by wheel slippage. For this reason, planetary rovers often utilize exteroceptive (e.g., vision-based) sensors. On the one hand, localization with proprioceptive sensors is straightforward, computationally efficient, and continuous. On the other …

Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak

Graduate Theses, Dissertations, and Problem Reports (ETD)

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, …

A Co-Optimal Coverage Path Planning Method For Aerial Scanning Of Complex Structures, Zhexiong Shang, Justin Bradley, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to …

Nonlinear Observer For Visual-Inertial Navigation Using Intermittent Landmark Measurements, Miaomiao Wang

Western Research Forum

The development of reliable orientation, position and linear velocity estimation algorithms for the 3D visual-inertial navigation system (VINS) is instrumental in many applications, such as autonomous underwater vehicles (AUVs), and unmanned aerial vehicles (UAVs). It is extremely important when the global position system (GPS) is not available in GPS-denied environments. Recently, observers design for VINS using landmark position measurements from Kinect sensors or stereo cameras has been increasingly investigated in the literature.

The aim of this work is to design a nonlinear observer for VINS under the assumption that landmark position measurements are intermittent. In practice, the landmark measurements are …

End To End Satellite Servicing And Space Debris Management, Aman Chandra, Himangshu Kalita, Roberto Furfaro, Jekan Thanga

Space Traffic Management Conference

There is growing demand for satellite swarms and constellations for global positioning, remote sensing and relay communication in higher LEO orbits. This will result in many obsolete, damaged and abandoned satellites that will remain on-orbit beyond 25 years. These abandoned satellites and space debris maybe economically valuable orbital real-estate and resources that can be reused, repaired or upgraded for future use. Space traffic management is critical to repair damaged satellites, divert satellites into warehouse orbits and effectively deorbit satellites and space debris that are beyond repair and salvage. Current methods for on-orbit capture, servicing and repair require a large service …

Immunity-Based Framework For Autonomous Flight In Gps-Challenged Environment, Mohanad Al Nuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about …