Browsing by Author "Leonessa, Alexander"

Now showing 1 - 20 of 155
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    Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®
    D'Angio, Paul Christopher (Virginia Tech, 2012-04-30)
    This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind. The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver. This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time. The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driver's hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driver's seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle.
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    Adaptive Control of the Transition from Vertical to Horizontal Flight Regime of a Quad-Tailsitter UAV
    Carter, Grant Inman (Virginia Tech, 2021-05-19)
    Tailsitter UAVs (Unmanned Aerial Vehicles) are a type of VTOL (Vertical Take off and Landing) aircraft that combines the agility of a quadrotor drone with the endurance and speed of a fixed-wing aircraft. For this reason, they have become popular in a wide range of applications from tactical surveillance to parcel delivery. This thesis details a clean sheet design process for a tailsitter UAV that includes the dynamic modeling, control design, simulation, vehicle design, vehicle prototype fabrication, and testing of a tailsitter UAV. The goal of this process was to design a robust controller that is able to handle uncertainties in the system's parameters and external disturbances and subsequently can control the vehicle through the transition between vertical and horizontal flight regimes. It is evident in the literature that most researchers choose to model and control tailsitter UAVs using separate methods for the vertical and horizontal flight regimes and combine them into one control architecture. The novelty of this thesis is the use of a single dynamical model for all flight regimes and the robust control technique used. The control algorithm used for this vehicle is a MRAC (Model Reference Adaptive Control) law, which relies on reference models and gains that adapt according to the vehicle's response in all flight regimes. To validate this controller, numerical simulations in Matlab and flight tests were conducted. The combination of these validation methods confirms our adaptive controller's ability to control the transition between the vertical and horizontal flight regimes when faced with both parametric uncertainties and external disturbances.
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    Adaptive Controller Development and Evaluation for a 6DOF Controllable Multirotor
    Furgiuele, Theresa Chung Wai (Virginia Tech, 2022-10-03)
    The omnicopter is a small unmanned aerial vehicle capable of executing decoupled translational and rotational motion (six degree of freedom, 6DOF, motion). The development of controllers for various 6DOF controllable multirotors has been much more limited than development for quadrotors, which makes selecting a controller for a 6DOF multirotor difficult. The omnicopter is subject to various uncertainties and disturbances from hardware changes, structural dynamics, and airflow, making adaptive controllers particularly interesting to investigate. The goal of this research is to design and evaluate the performance of various position and attitude controller combinations for the omnicopter, specifically focusing on adaptive controllers. Simulations are first used to compare combinations of three position controllers, PID, model reference adaptive control, augmented model reference adaptive control (aMRAC), and four attitude controllers, PI/feedback linearization (PIFL), augmented model reference adaptive control, backstepping, and adaptive backstepping (aBack). For the simulations, the omnicopter is commanded to point at and track a stationary aim point as it travels along a $C^0$ continuous trajectory and a trajectory that is $C^1$ continuous. The controllers are stressed by random disturbances and the addition of an unaccounted for suspended mass. The augmented model reference adaptive controller for position control paired with the adaptive backstepping controller for attitude control is shown to be the best controller combination for tracking various trajectories while subject to disturbances. Based on the simulation results, the PID/PIFL and aMRAC/aBack controllers are selected to be compared during three different flight tests. The first flight test is on a $C^1$ continuous trajectory while the omnicopter is commanded to point at and track a stationary aim point. The second flight test is a hover with an unmodeled added weight, and the third is a circular trajectory with a broken blade. As with the simulation results, the adaptive controller is shown to yield better performance than the nonadaptive controller for all scenarios, particularly for position tracking. With an added weight or a broken propeller, the adaptive attitude controller struggles to return to level flight, but is capable of maintaining steady flight when the nonadaptive controller tends to fail. Finally, while model reference adaptive controllers are shown to be effective, their nonlinearity can make them difficult to tune and certify via standard certification methods, such as gain and phase margin. A method for using time delay margin estimates, a potential certification metric, to tune the adaptive parameter tuning gain matrix is shown to be useful when applied to an augmented MRAC controller for a quadrotor.
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    Adaptive Firmware Framework for Microcontroller Development
    Tremaroli, Nicholas James (Virginia Tech, 2023-06-21)
    Firmware development for Low-Level Controllers is an extremely complex task. Single-threaded microcontrollers are most commonly used for these controllers and thus are only capable of executing a single task at a time. Microcontroller software tends to be designed for an extremely specific task with little room for scalability or code reuse. Additionally, the state of a microcontroller at run-time is very difficult to observe and thus makes it harder to debug and develop these control systems. To alleviate these development issues, a software framework was designed to simplify firmware development for Hardware Abstract Layered (HAL) control systems. The software framework was implemented on Texas Instruments TM4C123GXL Tivas on a multi-joint robot with the purpose of experimenting on a distributed microcontroller system. All of the software for the microcontroller was implemented into one program with initialization files from the high-level controller to configure each individual Tiva based on its functionality in the distributed system. The EtherCAT communication protocol is used primarily for its fast communication speed between high-level and low-level controllers. A basic GUI development environment accompanies the framework to aid in the initial development of a custom controller firmware and thus reduce development time. Additionally, this framework is designed to be easily scalable such that a real-time operating system (RTOS) can be implemented with minimal effort should the developer desire to do so. The proposed software framework thus overcomes major challenges when developing firmware for low-level controllers making development overall less time-consuming. Further, this framework can be used for many different robotic applications with a low-level multi-layered control architecture.
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    Adaptive Predictive Controllers for Agile Quadrupedal Locomotion with Unknown Payloads
    Amanzadeh, Leila (Virginia Tech, 2024-07-12)
    Quadrupedal robots play a vital role in various applications, from search and rescue operations to exploration in challenging terrains. However, locomotion tasks involving unknown payload transportation on rough terrains pose significant challenges, requiring adaptive control strategies to ensure stability and performance. This dissertation contributes to the advancement of adaptive motion planning and control solutions that enable quadrupedal robots to traverse unknown rough environments while tasked with transporting unknown payloads. In the first project, a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots is developed. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law applied to reduced-order locomotion (i.e., template) models. At the high level of the control hierarchy, an indirect adaptive law estimates unknown parameters of the reduced-order locomotion model under varying payloads, ensuring stability during trajectory planning. The optimal trajectories generated by the AMPC are then passed to a low-level and full-order nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations and hardware experiments on the A1 quadru[pedal robot validate the framework's capabilities, showcasing significant improvements in payload transportation on both flat and rough terrains compared to conventional MPC strategies. Specifically, the robot demonstrates proficiency in transporting unmodeled, unknown static payloads up to 109% of its own mass in experiments on flat terrains and 91% on rough experimental terrains. Moreover, the robot successfully manages dynamic payloads with 73% of its mass on rough terrains. Adaptive controllers must also address external disturbances inherent in real-world environments. Therefore, the second project introduces a hierarchical planning and control scheme with an adaptive L1 nonlinear model predictive control (ANMPC) at the high level, which integrates nonlinear MPC (NMPC) with an L1 adaptive controller. The prescribed optimal state and control input profiles generated by the ANMPC are then fed to the low-level nonlinear WBC. This approach aims to stabilize locomotion gaits in the presence of parametric uncertainties and external disturbances. The proposed controller is analyzed to accommodate uncertainties and external disturbances. Comprehensive numerical simulations and experimental validations on the A1 quadrupedal robot demonstrate its effectiveness on rough terrains. Numerical results suggest that ANMPC significantly improves the stability of the gaits in the presence of uncertainties and external disturbances compared to NMPC and AMPC. The robot can carry payloads up to 109% of its own mass on its trunk on flat and rough terrains. Simulation results show that the robot achieves a maximum payload capacity of 26.3 (kg), which is equivalent to 211% of its own mass on rough terrains with uncertainties and disturbances.
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    Adaptive Predictor-Based Output Feedback Control of Unknown Multi-Input Multi-Output Systems: Theory and Application to Biomedical Inspired Problems
    Nguyen, Chuong Hoang (Virginia Tech, 2016-06-03)
    Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties.
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    Adaptive Torque Control of a Novel 3D-Printed Humanoid Leg
    Hancock, Philip Jackson (Virginia Tech, 2020-07-23)
    In order to function safely in a dynamic environment with humans and obstacles, robots require active compliance control with force feedback. In these applications the control law typically includes full dynamics compensation to decouple the joints and cancel out nonlinearities, for which a high-fidelity model of the robot is required. In the case of a 3D-printed robot, components cannot be easily modeled due non-uniform densities, inconsistencies among the 3D printers used in manufacturing, and the use of different plastics with mechanical properties that are not widely known. To address this issue, this thesis presents an adaptive control framework which modifies the model parameters online in order to achieve satisfactory tracking performance. The inertial properties are estimated by adapting with respect to functions of the unknown parameters. This is achieved by rewriting the robot dynamics equations as the product of a matrix of known nonlinear functions of the joint states and a vector of constant unknowns. The result is a nonlinear system linearly parameterized in terms of the of the unknowns, which can be estimated using adaptation laws derived from Lyapunov stability theory. The proposed control system consists of an outer-loop impedance controller to regulate deviations from the nominal trajectory in the presence of disturbances, and an inner-loop force controller to track the joint torques commanded by the outer-loop. The proposed system is evaluated on an early prototype consisting of a 3DOF leg, and two actuator test setups for the low-level controller.
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    Adaptive, Anthropomorphic Robot Hands for Grasping and In-Hand Manipulation
    Kontoudis, Georgios Pantelis (Virginia Tech, 2019-02-01)
    This thesis presents the design, modeling, and development of adaptive robot hands that are capable of performing dexterous, in-hand manipulation. The robot hand comprises of anthropomorphic robotic fingers, which employ an adaptive actuation mechanism. The mechanism achieves both flexion/extension and adduction/abduction, on the finger's metacarpophalangeal joint, by using two actuators. Moment arm pulleys are employed to drive the tendon laterally, such that an amplification on the abduction motion occurs, while also maintaining the flexion motion. Particular emphasis has been given to the modeling and the analysis of the actuation mechanism. Also, a model for spatial motion is provided that relates the actuation modes with the finger motion and the tendon force with the finger characteristics. For the hand design, the use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. A design optimization framework assess the results of hand anthropometry studies to derive key parameters for the bio-inspired actuation design. The model assumptions are evaluated with the finite element method. The proposed finger has been fabricated with the Hybrid Deposition Manufacturing technique and the actuation mechanism's efficiency has been validated with experiments that include the computation of the finger workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the grasping and manipulation capabilities. Also, the hand design is fabricated with off-the-shelf materials and rapid prototyping techniques while its efficiency has been validated using an extensive set of experimental paradigms that involved the execution of grasping and in-hand manipulation tasks with everyday objects.
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    Adjustable Energy Saving Device for Transom Stern Hulls
    Salian, Rachit Pravin (Virginia Tech, 2019-05-10)
    The study presents a numerical investigation about the hydrodynamic characteristics of a transom mounted interceptor on the Oliver Hazard Perry class frigate (FFG-7), in order to assess the potential of propulsion power reduction in a wide range of speeds. This study is aimed to design a stern interceptor with optimal efficiency not only at top speed, but also cruising/transfer speeds, by a simple regulation of its variable geometrical characteristics (from a construction and operational standpoint). A high fidelity numerical model is developed in the open source CFD suite OpenFOAM for the prediction of the longitudinal dynamic equilibrium at speed and the total resistance characteristics of the bare hull. The Reynolds Averaged Navier-Stokes Equations are solved using interDyMFoam, a multiphase volume of fluid solver which allows for a dynamic mesh. The numerical model is validated using the results of the experimental model tests conducted on a 1/80th scale model at the United States Naval Academy Hydromechanics Laboratory (NAHL). The validated numerical model is used to predict the hydrodynamic characteristics of the transom mounted interceptor at different interceptor settings and speeds. The results show that the interceptor reduces the amount of resistance, the running trim, and the sinkage of the ship at high speeds. For a speed of 0.392 Froude number (Fr), a drag reduction of 3.76% was observed, as well as a significant reduction in trim.
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    Advanced Control Design of an Autonomous Line Painting Robot
    Cao, Mincan (Virginia Tech, 2017-05-30)
    Painting still plays a fundamental role in communication nowadays. For example, the paint on the road, called road surface marking, guides the traffic in order and maintains the high efficiency of the entire modern traffic system. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, the mechanical and electronic design of a Painting Robot was discussed. The overall design was to fulfill the requirements of the line painting. Computer vision techniques were applied to this thesis since the camera was selected as the major sensor of the robot. Advanced control theory was introduced to this thesis as well. Three different controllers were developed. The Proportional-Integral (PI) controller with an anti-windup feature was designed to overcome the drawbacks of the traditional PI controller. Model Reference Adaptive Control (MRAC) was introduced into this thesis to deal with the uncertainties of the system. At last, the hybrid PI-MRAC controller was implemented to maintain the advantages of both PI and MRAC approaches. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot.
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    Advanced Powertrain Design Using Model-Based Design
    Ord, David Andrew (Virginia Tech, 2014-06-23)
    The use of alternative fuels and advanced powertrain technologies has been increasing over the past few years as vehicle emissions and fuel economy have become prominent in both manufacturer needs and consumer demands. With more hybrids emerging from all automotive manufacturers, the use of computer modeling has quickly taken a lead in the testing of these innovative powertrain designs. Although on-vehicle testing remains an important part of the design process, modeling and simulation is proven to be an invaluable tool that can be applied anywhere from preliminary powertrain design to controller software validation. The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is applying for participation in the next Advanced Vehicle Technology Competition. EcoCAR 3 is a new four year competition sponsored by the Department of Energy and General Motors with the intention of promoting sustainable energy in the automotive sector. The goal of the competition is to guide students from universities in North America to create new and innovative technologies to reduce the environmental impact of modern day transportation. EcoCAR 3, like its predecessors, will give students hands-on experience in designing and implementing advanced technologies in a setting similar to that of current production vehicles. The primary goals of the competition are to improve upon a provided conventional, internal combustion engine production vehicle by designing and constructing a powertrain that accomplishes the following: • Reduce Energy Consumption • Reduce Well-to-Wheel (WTW) Greenhouse Gas (GHG) Emissions • Reduce Criteria Tailpipe Emissions • Maintain Consumer Acceptability in the area of Performance, Utility, and Safety • Meet Energy and Environmental Goals, while considering Cost and Innovation This paper presents a systematic approach in selecting a powertrain for HEVT to develop in the upcoming competition using model-based design. Using a base set of powertrain component models, several powertrain configurations are modeled and tested to show the progression from a basic conventional vehicle to several advanced hybrid vehicles. Each model is designed to generate energy consumption data, efficiency, emissions, as well as many other parameters that can be used to compare each of the powertrain configurations. A powertrain design is selected to meet the goals of the competition after exploring many powertrain configurations and energy sources. Three parallel powertrains are discussed to find a combination capable of meeting the target energy consumption and WTW GHG emissions while also meeting all of the performance goals. The first of these powertrains is sized to model a typical belted alternator starter (BAS) system and shows small improvements over a conventional vehicle. The next design is a parallel through the road hybrid that is sized to meet most power needs with an electric motor and a smaller IC engine. This case comes closer to the design goals, but still falls short on total energy consumption. Lastly, the battery and motor size are increased to allow a charge depleting mode, adding stored grid electricity to the energy sources. This electric energy only mode is able to displace a large amount of the fuel energy consumption based on the SAE J1711 method for determining utility factor weighted energy consumption of a plug-in hybrid vehicle. The final design is a Parallel Plug-In Hybrid Electric Vehicle using E85 fuel and a 7 kWh battery to provide an all-electric charge depleting range of 34 km (21 mi).
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    Affordable Haptic Gloves Beyond the Fingertips
    Ahn, Suyeon (Virginia Tech, 2023-10-11)
    With the increase in popularity of virtual reality (VR) systems, haptic devices have been garnering interest as means of augmenting users' immersion and experiences in VR. Unfortunately, most commercial gloves available on the market are targeted towards enterprise and research, and are too expensive to be accessible to the average consumer for entertainment. Some efforts have been made by gaming and do-it-yourself (DIY) enthusiasts to develop cheap, accessible haptic gloves, but due to cost limitations, the designs are often simple and only provide feedback at the fingertips. Considering the many types of grasps used by humans to interact with objects, it is evident that haptic gloves must offer feedback to many regions of the hand, such as the palm and lengths of the fingers to provide more realistic feedback. This thesis discusses a novel, affordable design that provides haptic feedback to the intermediate and proximal phalanges of the fingers (index, middle, ring and pinkie) using a ratchet and pawl actuation mechanism.
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    Analysis of Bat Biosonar Beampatterns: Biodiversity and Dynamics
    Caspers, Philip Bryan (Virginia Tech, 2017-01-24)
    Across species, bats exhibit wildly disparate differences in their noseleaf and pinnae shapes. Within Rhinolophid and Hipposiderid families, bats actively deform their pinnae and noseleaf during biosonar operation. Both the pinnae and noseleaf act as acoustic baffles which interact with the outgoing and incoming sound; thus, they form an important interface between the bat and its environment. Beampatterns describe this interface as joint time-frequency transfer functions which vary across spatial direction. This dissertation considers bat biosonar shape diversity and shape dynamics manifest as beampatterns. In the first part, the seemingly disparate set of functional properties resulting from diverse pinnae and noseleaf shape adaptations are considered. The question posed in this part is as follows: (i) what are the common properties between species beampatterns? and (ii) how are beampatterns aligned to a common direction for meaningful analysis? Hence, a quantitative interspecific analysis of the beampattern biodiversity was taken wherein: (i) unit[267]{} different pinnae and noseleaf beampatterns were rotationally aligned to a common direction and (ii) decomposed using principal component analysis, PCA. The first three principal components termed eigenbeams affect beamwidth around the single lobe, symmetric mean beampattern. Dynamic shape adaptations to the pinnae and noseleaf of the greater horseshoe bat (textit{Rhinolophus ferrumequinum}) are also considered. However, the underlying dynamic sensing principles in use are not clear. Hence, this work developed a biomimetic substrate to explore the emission and reception dynamics of the horseshoe bat as a sonar device. The question posed in this part was as follows: how do local features on the noseleaf and pinnae interact individually and when combined together to generate peak dynamic change to the incoming sonar information? Flexible noseleaf and pinnae baffles with different combinations of local shape features were developed. These baffles were then mounted to platforms to biomimetically actuate the noseleaf and pinnae during pulse emission and reception. Motions of the baffle surfaces were synchronized to the incoming and outgoing sonar waveform, and the time-frequency properties of the emission and reception baffles were characterized across spatial direction. Different feature combinations of the noseleaf and pinnae local shape features were ranked for overall dynamic effect.
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    Analysis of Sensing Technologies for Collision Avoidance for Small Rotary-Wing Uncrewed Aerial Vehicles
    Gandhi, Manav (Virginia Tech, 2022-06-22)
    As UASs (Uncrewed Aerial System) are further integrated into operations, the need for on-board environmental perception and sensing is necessitated. An accurate and reliable creation of a 3D map resembling an aircraft's surrounding is crucial for accurate collision avoidance and path planning. Consumer UASs are now being equipped with sensors to fulfill such a requirement – but no system has been proven as capable of being fully relied upon. With many sensing options available, there are several constraints regarding size, weight, and cost that must be considered when developing a sensing solution. Additionally, the robustness of the system must not be diminished when moving to a system that minimizes size, weight, or cost. An analysis of different sensing technologies that small rotary-wing aircraft (below 25kg) can be outfitted with for collision avoidance is performed. Several sensing technologies are initially compared through technology analyses and controlled experiments. The topmost systems were then further integrated onto a small low-cost quadcopter for flight testing and data acquisition. Ultimately, a fusion between stereo vision imagery and radar was deemed the most reliable method for providing environmental data needed for collision avoidance.
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    Analyzing the complexity of bat flight to inspire the design of flapping-flight drones
    Tyler, Adam Anthony Murphrey (Virginia Tech, 2024-08-22)
    With their exceptionally maneuverable flapping flight, bats could serve as a model for enhancing the flight abilities for future drones. However, bat flight is extremely complex and there are many engineering restrictions that prevent a flapping-flight drone from replicating the many degrees of freedoms present in biology. Hence, to make design choices of which properties in a bats wing kinematics should be reproduced, the present research has evaluated two metrics from information and complexity theory to identify which regions of the bat flight apparatus are most complex and where coupling across features of the bat flight kinematics exists. The values were the complexity metric as a measure of variability and mutual information as a measure of coupling. Both measures were applied to ten experimentally obtained digital models of the flight kinematics in Ridley's leaf-nosed bats as well as the simulated kinematics of a flapping-flight drone inspired by the same bat type. The pilot results obtained indicate that both measures could be useful to discover which elements of flight kinematics should be looked into for understanding and reproducing the maneuvering flight of bats. However, a functional interpretation will require complementary, e.g., aerodynamic metrics.
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    Anomaly detection in rolling element bearings via two-dimensional Symbolic Aggregate Approximation
    Harris, Bradley William (Virginia Tech, 2013-05-26)
    Symbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems.  Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise.  Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance.  Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs.  This thesis presents a novel way to detect bearing vibrational anomalies through Symbolic Aggregate Approximation (SAX) in the two-dimensional time-frequency domain.  SAX reduces computational requirements by partitioning high-dimensional sensor data into discrete states.  This analysis specifically suits bearing vibration data in the time-frequency domain, as the distribution of data does not greatly change between normal and faulty conditions. Under ground truth synthetically-generated experiments, two-dimensional SAX in conjunction with Markov model feature extraction is successful in detecting anomalies (> 99%) using short time spans (< 0.1 seconds) of data in the time-frequency domain with low false alarms (< 8%).  Analysis of real-world datasets validates the performance over the commonly used one-dimensional symbolic analysis by detecting 100% of experimental anomalous vibration with 0 false alarms in all fault types using less than 1 second of data for the basis of 'normality'. Two-dimensional SAX also demonstrates the ability to detect anomalies in predicative monitoring environments earlier than previous methods, even in low Signal-to-Noise ratios.
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    Applied Nonlinear Control of Unmanned Vehicles with Uncertain Dynamics
    Morel, Yannick (Virginia Tech, 2009-04-17)
    The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system's dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced.
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    Assessing Limb Symmetry using the Clinically Accessible loadsol®
    Renner, Kristen Elizaberth (Virginia Tech, 2019-04-23)
    Decreased gait symmetry has been correlated with an increased fall risk, abnormal joint loading and decreased functional outcomes. Therefore, symmetry is focused on in the rehabilitation of many patient populations. Currently, load based symmetry is collected using expensive and immobile devices that are not clinically accessible, but there is a clinical need for an objective measure of loading symmetry during daily tasks like walking. Therefore, the purpose of this dissertation was to 1) assess the validity and reliability of the loadsol® to capture ground reaction force data, 2) use the loadsol® to determine the differences in symmetry between adults with a TKA and their healthy peers and 3) explore the potential of a commercially available biofeedback system to acutely improve gait symmetry in adults. The results of this work indicate that the loadsol® is a valid and reliable method of collecting loading measures during walking in both young and older adults. TKA patients who are 12-24 months post-TKA have lower symmetry in the weight acceptance peak force, propulsive peak force and impulse when compared to their healthy peers. Finally, a case study with four asymmetric adults demonstrated that a 10-minute biofeedback intervention with the loadsol® resulted in an acute improvement in symmetry. Future work is needed to determine the potential of this intervention to improve symmetry in patient populations and to determine whether the acute response is retained following the completion of the intervention.
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    Autonomous Fire Suppression Using Feedback Control for Robotic Firefighting
    McNeil, Joshua G. (Virginia Tech, 2016-02-04)
    There is an increasing demand for robotics in dangerous and extreme conditions to limit human exposure and risk. An area in which robots are being considered as a support tool is in firefighting operations to reduce the number of firefighter injuries and deaths. One such application is to increase firefighting performance through localized fire suppression. This research focused on developing an autonomous suppression system for use on a mobile robotic platform. This included a real-time close proximity fire suppression approach, appropriate feature selection and probabilistic classification of water leaks and sprays, real-time trajectory estimation, and a feedback controller for error correction in longer-range firefighting. The close proximity suppression algorithm uses IR fire detection IR stereo processing to localize a fire. Feedback of the fire size and fire target was used to manipulate the nozzle for effective placement of the suppressant onto the fire and experimentally validated with tests in high and low visibility environments. To improve performance of autonomous suppression and for inspection tasks, identification of water sprays and leaks is a critical component. Bayesian classification was used to identify the features associated with water leaks and sprays in thermal images. Appropriate first and second order features were selected by using a multi-objective genetic algorithm optimization. Four textural features were selected as a method of discriminating water sprays and leaks from other non-water, high motion objects. Water classification was implemented into a real-time suppression system as a method of determining the yaw and pitch angle of a water nozzle. Estimation of the angle orientation provided an error estimate between the current path and desired nozzle orientation. A proportional-integral (PI) controller was used to correct for forced errors in fire targeting and performance and response was shown through indoor and outdoor suppression tests with wood-crib fires. The autonomous suppression algorithm was demonstrated through fire testing to be at least three times faster compared with suppression by an operator using tele-operation.
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    Autonomous Mobile Robot Navigation in Dynamic Real-World Environments Without Maps With Zero-Shot Deep Reinforcement Learning
    Sivashangaran, Shathushan (Virginia Tech, 2024-06-04)
    Operation of Autonomous Mobile Robots (AMRs) of all forms that include wheeled ground vehicles, quadrupeds and humanoids in dynamically changing GPS denied environments without a-priori maps, exclusively using onboard sensors, is an unsolved problem that has potential to transform the economy, and vastly improve humanity's capabilities with improvements to agriculture, manufacturing, disaster response, military and space exploration. Conventional AMR automation approaches are modularized into perception, motion planning and control which is computationally inefficient, and requires explicit feature extraction and engineering, that inhibits generalization, and deployment at scale. Few works have focused on real-world end-to-end approaches that directly map sensor inputs to control outputs due to the large amount of well curated training data required for supervised Deep Learning (DL) which is time consuming and labor intensive to collect and label, and sample inefficiency and challenges to bridging the simulation to reality gap using Deep Reinforcement Learning (DRL). This dissertation presents a novel method to efficiently train DRL with significantly fewer samples in a constrained racetrack environment at physical limits in simulation, transferred zero-shot to the real-world for robust end-to-end AMR navigation. The representation learned in a compact parameter space with 2 fully connected layers with 64 nodes each is demonstrated to exhibit emergent behavior for Out-of-Distribution (OOD) generalization to navigation in new environments that include unstructured terrain without maps, dynamic obstacle avoidance, and navigation to objects of interest with vision input that encompass low light scenarios with the addition of a night vision camera. The learned policy outperforms conventional navigation algorithms while consuming a fraction of the computation resources, enabling execution on a range of AMR forms with varying embedded computer payloads.