Browsing by Author "Akbari Hamed, Kaveh"

Now showing 1 - 20 of 43
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    Adaptive Communication Interfaces for Human-Robot Collaboration
    Christie, Benjamin Alexander (Virginia Tech, 2024-05-07)
    Robots can use a collection of auditory, visual, or haptic interfaces to convey information to human collaborators. The way these interfaces select signals typically depends on the task that the human is trying to complete: for instance, a haptic wristband may vibrate when the human is moving quickly and stop when the user is stationary. But people interpret the same signals in different ways, so what one user finds intuitive another user may not understand. In the absence of task knowledge, conveying signals is even more difficult: without knowing what the human wants to do, how should the robot select signals that helps them accomplish their task? When paired with the seemingly infinite ways that humans can interpret signals, designing an optimal interface for all users seems impossible. This thesis presents an information-theoretic approach to communication in task-agnostic settings: a unified algorithmic formalism for learning co-adaptive interfaces from scratch without task knowledge. The resulting approach is user-specific and not tied to any interface modality. This method is further improved by introducing symmetrical properties using priors on communication. Although we cannot anticipate how a human will interpret signals, we can anticipate interface properties that humans may like. By integrating these functional priors in the aforementioned learning scheme, we achieve performance far better than baselines that have access to task knowledge. The results presented here indicate that users subjectively prefer interfaces generated from the presented learning scheme while enabling better performance and more efficient interactions.
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    Adaptive Longitudinal and Lateral Control for Autonomous Vehicles: High-Speed Platooning of Articulated Trucks
    Shaju, Aashish (Virginia Tech, 2024-12-13)
    Autonomous vehicle technology has seen remarkable advancements in recent years, yet significant challenges remain in ensuring robust, adaptive, and efficient control algorithms for diverse operational scenarios. This dissertation aims to address these challenges by developing and validating a generic control framework that is applicable to both independent autonomous vehicles and connected vehicle systems such as automated platoons. The versatility of the proposed framework ensures its applicability to a wide range of vehicles, including automobiles, light trucks, and rigid and articulated commercial trucks, under high-speed and complex driving conditions. The first major contribution is the development of a longitudinal control algorithm based on a nested PID structure. Designed for computational efficiency and stability, the algorithm simultaneously regulates vehicle speed and inter-vehicle distance. Its adaptability is extended to curved trajectories using an arc length-based error calculation, making it suitable for real-world scenarios. A rigorous simulation study is undertaken to demonstrate the algorithm's stability and robustness to parametric uncertainties. The second major contribution is the development of a high-speed lateral control algorithm based on a modified clothoid controller. This lateral control framework is designed to minimize lateral acceleration (improving passenger comfort and safety) and reduce cross-track errors (CTEs) across various vehicle configurations, including articulated trucks. Simulation results confirmed the superiority of the clothoid-based controller in minimizing CTEs and maintaining smooth steering profiles, even for complex vehicle configurations. Notably, tracking the steer axle center was found to significantly improve performance across all trajectory segments. The final contribution integrates the longitudinal and lateral control frameworks, enabling seamless operation in automated platooning scenarios. This integration requires adapting the longitudinal controller to curved trajectories using arc length-based calculations. Comprehensive simulations, including challenging trajectories such as dual lane changes, and actual roadways like sections of the Blue Ridge Parkway in Virginia and South Grade Road in California, validated the integrated framework. Despite minor anomalies in high-stress conditions, the results demonstrate acceptable performance in terms of spacing errors, relative velocities, lateral accelerations, and CTEs, highlighting the robustness and resilience of the proposed system. The study presents a unified control framework that bridges the gap between independent autonomous vehicles and connected vehicle systems. The generic nature of the algorithms ensures their applicability to a wide variety of vehicles and scenarios, making them a strong candidate for future deployment in autonomous systems. The findings represent significant advances toward safer, more efficient, and versatile autonomous vehicle technologies, addressing critical challenges in the path to commercial implementation
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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 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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    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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    Application of Optical Detection Methods for Top-of-Rail (TOR) Lubricity Evaluation on a Moving Platform for Revenue Service Track
    Mast, Timothy Edward (Virginia Tech, 2020-04-17)
    This research serves to evaluate the ability of optical detection techniques to ascertain the lubricity of revenue service track from a moving platform for railroad applications. A literature review is presented that covers the rail vehicle dynamics that drive the need of Top-of-Rail lubrication and directly affect the manner in which the Top-of-Rail Friction Modifiers (TORFM) and flange grease both spread down rail and eventually wear away. This literature review also highlights previous research in the field of rail lubrication and the benefits that rail lubricants, specifically TORFM, provide for the railroads. Finally, the literature review covers the governing optical principals inherent to the synchronous spot radiometer that has been developed for use in the research as a gloss ratio instrument and also addresses the drawbacks and challenges inherent to applying this type of instrument in the railroad industry. The research then overviews previous rail lubricity sensors developed by the Railway Technologies Laboratory (RTL) at Virginia Tech and the lessons learned from their application. The preceding field testing conducting with a modified second generation rail lubricity sensor and a rail push car is briefly summarized with emphasis on the drawbacks and issues that were used to develop the third generation sensor used for this research. The development of the third generation sensor is covered, including the issues that it attempts to solve from its predecessor and the governing optical principals that govern the operation of the sensor. The laboratory evaluations conducting to commission the sensor are also covered in preparation for deploying the new third generation sensor in medium speed, medium distance revenue service testing. This includes a shakedown run on a siding in Riverside, VA prior to conducting mainline in-service testing. Finally, this research thesis covers the in-service testing on revenue track conducted with the new third generation rail lubricity sensor and the accompanying remote-controlled (RC) rail cart. The two components, when combined, create a Lubricity Assessment System which is capable of being operated at speeds upwards of 10 mph remotely from a follow hy-rail truck. The data collected from this field test is analyzed for the lubricity assessments that are able to be drawn from this initial phase of field service testing. The conclusions from this testing affirm the ability of optical methods to determine and evaluate Top-of-Rail (TOR) lubricity from a moving platform. Specifically, the new sensor is able to identify several local phenomena that demonstrate the high potential for errant evaluation of rail lubricity evaluation from spot check based methods that are solved by evaluating the track in a continuous, moving fashion. Based on the continuous moving data collected for this test, several new signal traits such as the spatial frequency (wavenumber) associated with the passing freight cart wheels in the lubricity signal and the phantom applicator effect of transient lubricity conditions at the entrances and exits of curves can be detected and investigated. The success of this research indicates the continued evaluation of lubricity signals from a moving platform is warranted and suggests the potential for introducing one of these systems to various track metrology cars deployed throughout the United States railroads.
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    Autonomous Robotic Escort Incorporating Motion Prediction with Human Intention
    Conte, Dean Edward (Virginia Tech, 2021-03-02)
    This thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate.
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    Bat Inspired Lifesize Ornithopter with Passive Lateral Wing Retraction
    Kelley, Logan Chaney (Virginia Tech, 2024-05-31)
    Bats have a unique flying style that allows them to be highly dexterous in capturing prey and have great freedom of movement in flight. Bats' wings have a wing membrane that is tensioned by their fingers and arms, allowing them to retract their wings laterally in flight. This distinct motion has allowed bats to be the only mammals capable of sustained flight, adding to their evolutionary uniqueness. This thesis presents the creation of the VALKRIE (Versatile Aerial Lifesize Kinetic Robot Inspired by bat Evolution) project: a to-scale simplified bat-inspired ornithopter that can be remotely controlled, sustain flight, and passively retract and extend its wings laterally. VALKRIE mimics the dimensions and size of its biological counterpart, Hipposideros diadema, a medium-sized bat; setting its aerodynamical constraints to the dimensions of Hipposideros diadema. Bats' maneuverability is derived from their unique wing motion while in flight, retracting and extending their wings. VALKRIE mimics this motion by simplifying the joint structure of a bat's wing and passively retracting and extending the wings. By simplifying the complex anatomy of bat wing motion, VALKRIE can maintain flight and generate sufficient lift for increasing altitude. With a simplified design, VALKRIE only has two motors that actuate wing flapping, wing retraction, and rotation of the hind legs. With this simplified design, the operator can remotely control VALKRIE by increasing and decreasing the wingbeat frequency and steering to the right and left with the hind legs.
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    Collaborative Locomotion of Quadrupedal Robots: From Centralized Predictive Control to Distributed Control
    Kim, Jeeseop (Virginia Tech, 2022-08-26)
    This dissertation aims to realize the goal of deploying legged robots that cooperatively walk to transport objects in complex environments. More than half of the Earth's continent is unreachable to wheeled vehicles---this motivates the deployment of collaborative legged robots to enable the accessibility of these environments and thus bring robots into the real world. Although significant theoretical and technological advances have allowed the development of distributed controllers for complex robot systems, existing approaches are tailored to the modeling and control of multi-agent systems composed of collaborative robotic arms, multi-fingered robot hands, aerial vehicles, and ground vehicles, but not collaborative legged agents. Legged robots are inherently unstable, unlike most of the systems where these algorithms have been deployed. Models of cooperative legged robots are further described by high-dimensional, underactuated, and complex hybrid dynamical systems, which complicate the design of control algorithms for coordination and motion control. There is a fundamental gap in knowledge of control algorithms for safe motion control of these inherently unstable hybrid dynamical systems, especially in the context of collaborative work. The overarching goal of this dissertation is to create a formal foundation based on scalable optimization and robust and nonlinear control to develop distributed and hierarchical feedback control algorithms for cooperative legged robots to transport objects in complex environments. We first develop a hierarchical nonlinear control algorithm, based on model predictive control (MPC), quadratic programming (QP), and virtual constraints, to generate and stabilize locomotion patterns in a real-time manner for dynamical models of single-agent quadrupedal robots. The higher level of the proposed control scheme is developed based on an event-based MPC that computes the optimal center of mass (COM) trajectories for a reduced-order linear inverted pendulum (LIP) model subject to the feasibility of the net ground reaction force (GRF). QP-based virtual constraint controllers are developed at the lower level of the proposed control scheme to impose the full-order dynamics to track the optimal trajectories while having all individual GRFs in the friction cone. The analytical results are numerically verified to demonstrate stable and robust locomotion of a 22 degree of freedom (DOF) quadrupedal robot, in the presence of payloads, external disturbances, and ground height variations. We then present a hierarchical nonlinear control algorithm for the real-time planning and control of cooperative locomotion of legged robots that collaboratively carry objects. An innovative network of reduced-order models subject to holonomic constraints, referred to as interconnected LIP dynamics, is presented to study quasi-statically stable cooperative locomotion. The higher level of the proposed algorithm employs a supervisory controller, based on event-based MPC, to effectively compute the optimal reduced-order trajectories for the interconnected LIP dynamics. The lower level of the proposed algorithm employs distributed nonlinear controllers to reduce the gap between reduced- and full-order complex models of cooperative locomotion. We numerically investigate the effectiveness of the proposed control algorithm via full-order simulations of a team of collaborative quadrupedal robots, each with a total of 22 DOFs. The dissertation also investigates the robustness of the proposed control algorithm against uncertainties in the payload mass and changes in the ground height profile. Finally, we present a layered control approach for real-time trajectory planning and control of dynamically stable cooperative locomotion by two holonomically constrained quadrupedal robots. An innovative and interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control scheme, two different MPC algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The MPCs compute the reduced-order states, GRFs, and interaction wrenches between the agents. The distributed MPC assumes two local QPs that share their optimal solutions according to a one-step communication delay and an agreement protocol. At the lower level of the control scheme, distributed nonlinear controllers are employed to impose the full-order dynamics to track the prescribed and optimal reduced-order trajectories and GRFs. The effectiveness of the proposed layered control approach is verified with extensive numerical simulations and experiments for the blind, robust, and cooperative locomotion of two holonomically constrained A1 robots with different payloads on different terrains and in the presence of external disturbances. It is shown that the distributed MPC has a performance similar to that of the centralized MPC, while the computation time is reduced significantly.
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    Communication-Driven Robot Learning for Human-Robot Collaboration
    Habibian, Soheil (Virginia Tech, 2024-07-25)
    The growing presence of modern learning robots necessitates a fundamental shift in design, as these robots must learn skills from human inputs. Two main components close the loop in a human-robot interaction: learning and communication. Learning derives robot behaviors from human inputs, and communication conveys information about the robot's learning to the human. This dissertation focuses on methods that enable robots to communicate their internal state clearly while learning precisely from human inputs. We first consider the information implicitly communicated by robot behavior during human interactions and whether it can be utilized to form human-robot teams. We investigate behavioral economics to identify biases and expectations in human team dynamics and incorporate them into human-robot teams. We develop and demonstrate an optimization approach that relates high-level subtask allocations to low-level robot actions, which implicitly communicates learning to encourage human participation in robot teams. We then study how communication helps humans teach tasks to robots using active learning and interactive imitation learning algorithms. Within the active learning approach, we develop a model that forms a belief over the human's mental model about the robot's learning. We validate that our algorithm enables the robot to balance between learning human preferences and implicitly communicating its learning through questions. Within the imitation learning approach, we integrate a wrapped haptic display that explicitly communicates representations from the robot's learned behavior to the user. We show that our framework helps the human teacher improve different aspects of the robot's learning during kinesthetic teaching. We then extend this system to a more comprehensive interactive learning architecture that provides multi-modal feedback through augmented reality and haptic interfaces. We present a case study with this closed-loop system and illustrate improved teaching, trust, and co-adaptation as the measured benefits of communicating robot learning. Overall, this dissertation demonstrates that bi-directional communication helps robots learn faster and adapt better, while humans experience a more intuitive and trust-based interaction.
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    Cooperative Clothoidal-Estimation Based Lane Detection For Vehicle Platooning
    Hunde, Sena Aschalew (Virginia Tech, 2021-06-09)
    Vehicle platooning is an advanced vehicle maneuver that allows for the simultaneous control of several vehicles traveling on the roadway cite{al2010experimental}. Automated platoons, when activated in tractor trailer convoys, have a high potential of increasing the fuel efficiency and improving the utilization of roadways by allowing more vehicles to share the road at the same time. The increased fuel efficiency translates to lower cost on goods and motivates a more environmentally friendly and sustainable economy. In order to achieve the promised fuel savings from vehicle platooning, the vehicles need to follow each other at shorter headways than in typical driving scenarios. The reduced separation distance between the lead and follow vehicle reduces visibility and the reaction time available for the follow vehicle; this renders most modern Active Driver Assist Systems (ADAS) ineffective since they are not designed for operation in such short headway conditions. The focus of this work is related to understanding and improving the failures of Lane Keep Assist (LKA) systems in the follow vehicles of a platoon. In this work, the source of lane detection degradation when using a monocular forward facing camera in short headway platooning is identified. Furthermore, a novel lane augmentation algorithm is proposed to improve the lane detection capability of follow vehicles in a platoon. The lane augmentation process utilizes a longitudinal transformation of lane parameters from the lead to the follow vehicles. The transformation utilizes an accurate understanding of the relative spatial position and orientation of the two vehicles. The transformation also requires a reliable communication system between the two vehicles such as a Vehicle-to-Vehicle (V2V) module. The work presented in this thesis develops theory, simulation and verification using real world data of the proposed cooperative lane augmentation. The results of this work indicate that it is possible to improve vehicle platooning performance by distributing the required sensing across multiple agents of the platoon.
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    Cooperative Perception for Connected Vehicles
    Mehr, Goodarz (Virginia Tech, 2024-05-31)
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    Data driven modeling and MPC Based control for Pathological Tremors
    Samal, Subham Swastik (Virginia Tech, 2024-12-19)
    Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. For improved performance of such devices, we need to solve a few problems, which include developing a model for pathological tremors, and a safe control system that can conveniently incorporate constraints on the wrist's range of motion and it's input force/torque. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses. Existing methods are either too general or too simplistic to accurately predict these tremors in the long term, motivating us to explore better modeling of tremors for long-term predictions and analysis. We explore towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20- 100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinson's tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions. Then, we explore the application of model-based predictive control (MPC) for the full wrist exoskeleton designed in our lab for the alleviation of tremors. The main motivation for using MPC here relies on its ability to incorporate state and input constraints, which are crucial for the user's safety. We employ a linear MPC methodology, in which the forearm-exoskeleton model is successively linearized at each time sample to obtain a linear state space model, which is then used to obtain the optimal input by minimizing a convex quadratic cost function. This is then integrated with the tremor model developed via BMFLC and neural networks to provide tremor suppression. Simulation studies are provided to demonstrate the effectiveness of the control schemes. The numerical simulations suggest that the MPC framework is capable of accurate trajectory tracking while providing better tremor suppression than a PD controller without using any tremor model, while the neural network model outperforms the frequency-based BMFLC model. The findings could set up for devising physics-based Neural networks for pathological tremor modeling and experimentally evaluate the performance of the developed framework.
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    Design and Development of an Autonomous Line Painting System
    Nagi, Navneet Singh (Virginia Tech, 2019-02-08)
    With vast improvements in computing power in the last two decades, humans have invested significantly in engineering resources in an attempt to automate labor intensive or dangerous tasks. A particularly dangerous and labor-intensive task is painting lines on roads for facilitating urban mobility. This thesis proposes an approach to automate the process of painting lines on the ground using an autonomous ground vehicle (AGV) fitted with a stabilized painting mechanism. The AGV accepts Global Positioning System (GPS) coordinates for waypoint navigation. A computer vision algorithm is developed to provide vision feedback to stabilize the painting mechanism. The system is demonstrated to follow an input desired trajectory and cancel any high frequency vibrations due to the uneven terrain that the vehicle is traversing. Also, the stabilizing system is able to eliminate the long-term drift (due to inaccurate GPS waypoint navigation) using the complementary vision system.
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    Design and Evaluation of an Underactuated Lower Body Exoskeleton
    Biggers, Zackory James (Virginia Tech, 2022-06-08)
    An underactuated exoskeleton design for walking assistance is presented and evaluated. The exoskeleton uses one motor per leg and makes use of a pantograph to reduce the overall profile and allow the exoskeleton to closely follow the shape of the user's leg. Support is provided between the ball of the user's foot and their waist by compressing a spring in parallel with the user's leg during Stance Phase. The exoskeleton has a mass of 14.0 kg (30.8 lbs) and was tested up to a supplied spring force of 323.6 N (72.75 lbf) which equates to around 161.8 N (36.38 lbf) of assistive force at the waist. Range of motion tests showed minimal restriction at the knee and ankle, but some restriction of the hip. Human subject experiments using a simple gait detection method based on GRF at walking speeds from 0.45 m/s to 1.12 m/s (1.0 mph to 2.5 mph) were performed and showed an increase in the time between actual heel strike and predicted heel strike of approximately 0.05 seconds to 0.1 seconds. Lastly, calculations are presented examining the effect of exoskeleton assistance on the biological joint moments and optimizing the actuator design to reduce power consumption. The actual performance of the exoskeleton is compared with the calculations based on the joint angles during a typical walking cycle.
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    Design of Time-Varying Hybrid Zero Dynamics Controllers for Exponential Stabilization of Agile Quadrupedal Locomotion
    Martin, Joseph Bacon V (Virginia Tech, 2020-10-23)
    This thesis explores the development of time-varying virtual constraint controllers that allow stable and agile gaits for full-order hybrid dynamical models of quadrupedal locomotion. Unlike time-invariant nonlinear controllers, time-varying controllers do not rely on sensor data for gait phasing and can initiate locomotion from zero velocity. Motivated by these properties, we investigate the stability guarantees that can be provided by the time-varying approach. More specifically, we systematically establish necessary and sufficient conditions that guarantee exponential stability of periodic orbits for time-varying hybrid dynamical systems utilizing the Poincar� return map. Leveraging the results of the presented proof, we develop time-varying virtual constraint controllers to stabilize bounding, trotting, and walking gaits of a 14 degree of freedom quadrupedal robot, Minitaur. A framework for selecting the parameters of virtual constraint controllers to achieve exponential stability is shown, and the feasibility of the analytical results is numerically validated in full-order model simulations of Minitaur.
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    The Development of a Locomotion Interface for Virtual Reality Terrain Simulation
    He, An Chi (Virginia Tech, 2025-01-09)
    Virtual reality (VR) technology has amazed us with its capability of blurring the boundary between real and virtual; it tricks our minds into believing that what we experience is real. This technology is used for entertainment and various applications such as rehabilitation, immersive training, 3D design, etc. However, navigating the VR world remains a significant issue that has yet to be solved. In VR, we often rely on controllers or joysticks to traverse the virtual world. VR navigation with joysticks creates a discrepancy between the visual and bodily senses, which creates extra cognitive load and other problems, such as VR motion sickness, which limit the long-term use of VR applications and prevent it from being widely adopted. Walking is the most common task that we perform every day; it is the most intuitive way of navigation and is desirable to be implemented in VR. This dissertation details the development of a device that enables walking in VR and allows users to experience different terrains. Researchers and companies have pulled off endeavors to enable locomotion in VR; some even achieved commercial success, like the Omni One or KAT VR treadmill. Still, most devices are limited in simulating flat-ground walking without the capability of displaying any terrain, which is far from our living environments and restricts its usage. Our living surroundings feature stairs, bumps, and slopes that those devices can not render. Furthermore, VR can be desirable in hazardous reaction training, which is often situated in highly unstructured environments. Being able to simulate various terrains not only enhances VR immersion but also extends its usage for simulating multiple scenarios that could require extensive cost to construct. This document presents the design, build, control, and evaluation of a robotic locomotion interface that aims to display computer-generated terrains, allowing realistic lower-body engagement. The device features a novel, compact design that allows it to be available in space-constrained places like rehabilitation clinics or smaller labs. It uses design guidelines derived from motion capture data with dynamic simulation to align the robot and human workspace. After that, the system framework is addressed from both hardware and software aspects. This device features open-source Institute for Human and Machine Cognition (IHMC) software integrated with the Simple Open Source EtherCAT Master (SOEM) library to execute real-time EtherCAT communication. An admittance controller has been implemented to achieve smooth physical human-robot interaction (pHRI), governing the robot motion according to user input force. This work presents measurements to evaluate the system's performance. The document presents a CoM (center-of-mass) estimation algorithm that is based on LIP (linear inverted pendulum) model and ZMP (zero-moment point). And the estimation method is further validated through two applications: an initial framework of tele-locomotion and VR interaction. In the first case, it uses estimated CoM motion as a tracking reference for humanoid robots. The second application presents a framework that is able to display virtual terrain in the physical world.
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    Distributed Feedback Control Algorithms for Cooperative Locomotion: From Bipedal to Quadrupedal Robots
    Kamidi, Vinaykarthik Reddy (Virginia Tech, 2022-03-25)
    This thesis synthesizes general and scalable distributed nonlinear control algorithms with application to legged robots. It explores both naturally decentralized problems in legged locomotion, such as the collaborative control of human-lower extremity prosthesis and the decomposition of high-dimensional controllers of a naturally centralized problem into a net- work of low-dimensional controllers while preserving equivalent performance. In doing so, strong nonlinear interaction forces arise, which this thesis considers and sufficiently addresses. It generalizes to both symmetric and asymmetric combinations of subsystems. Specifically, this thesis results in two distinct distributed control algorithms based on the decomposition approach. Towards synthesizing the first algorithm, this thesis presents a formal foundation based on de- composition, Hybrid Zero Dynamics (HZD), and scalable optimization to develop distributed controllers for hybrid models of collaborative human-robot locomotion. This approach con- siders a centralized controller and then decomposes the dynamics and parameterizes the feedback laws to synthesize local controllers. The Jacobian matrix of the Poincaré map with local controllers is studied and compared with the centralized ones. An optimization problem is then set up to tune the parameters of the local controllers for asymptotic stability. It is shown that the proposed approach can significantly reduce the number of controller parameters to be optimized for the synthesis of distributed controllers, deeming the method computationally tractable. To evaluate the analytical results, we consider a human amputee with the point of separation just above the knee and assume the average physical parameters of a human male. For the lower-extremity prosthesis, we consider the PRleg, a powered knee-ankle prosthetic leg, and together, they form a 19 Degrees of Freedom (DoF) model. A multi-domain hybrid locomotion model is then employed to rigorously assess the performance of the afore-stated control algorithm via numerical simulations. Various simulations involving the application of unknown external forces and altering the physical parameters of the human model unbeknownst to the local controllers still result in stable amputee loco- motion, demonstrating the inherent robustness of the proposed control algorithm. In the later part of this thesis, we are interested in developing distributed algorithms for the real-time control of legged robots. Inspired by the increasing popularity of Quadratic programming (QP)-based nonlinear controllers in the legged locomotion community due to their ability to encode control objectives subject to physical constraints, this thesis exploits the idea of distributed QPs. In particular, this thesis presents a formal foundation to systematically decompose QP-based centralized nonlinear controllers into a network of lower-dimensional local QPs. The proposed approach formulates a feedback structure be- tween the local QPs and leverages a one-step communication delay protocol. The properties of local QPs are analyzed, wherein it is established that their steady-state solutions on periodic orbits (representing gaits) coincide with that of the centralized QP. The asymptotic convergence of local QPs' solutions to the steady-state solution is studied via Floquet theory. Subsequently, to evaluate the effectiveness of the analytical results, we consider an 18 DoF quadrupedal robot, A1, as a representative example. The network of distributed QPs mentioned earlier is condensed to two local QPs by considering a front-hind decomposition scheme. The robustness of the distributed QP-based controller is then established through rigorous numerical simulations that involve exerting unmodelled external forces and intro- ducing unknown ground height variations. It is further shown that the proposed distributed QPs have reduced sensitivity to noise propagation when compared with the centralized QP. Finally, to demonstrate that the resultant distributed QP-based nonlinear control algorithm translates equivalently well to hardware, an extensive set of blind locomotion experiments on the A1 robot are undertaken. Similar to numerical simulations, unknown external forces in the form of aggressive pulls and pushes were applied, and terrain uncertainties were introduced with the help of arbitrarily displaced wooden blocks and compliant surfaces. Additionally, outdoor experiments involving a wide range of terrains such as gravel, mulch, and grass at various speeds up to 1.0 (m/s) reiterate the robust locomotion observed in numerical simulations. These experiments also show that the computation time is significantly dropped when the distributed QPs are considered over the centralized QP.
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    Dynamic Modeling and Lateral Stability Analysis of Long Combination Vehicles
    Zhang, Zichen (Virginia Tech, 2022-10-28)
    This study provides a comprehensive modeling evaluation of the dynamic stability of Long Combination Vehicles (LCVs) that are commonly operated on U.S. highways, using multibody dynamic simulations in MATLAB/Simulink®. The dynamic equations for a tractor with two trailers connected by an A-frame converter dolly (A-Dolly) are developed. The dynamic model is used for running MATLAB® simulations, with parameters that are obtained through measurements or obtained from other sources. The simulation results are verified using track test data to establish a baseline model. The baseline model is used for parametric studies to evaluate the effect of trailer cargo weight, center of gravity (CG) longitudinal location, and trailer wheelbase. The dynamic model is further used to analyze both single-trailer and double-trailer trucks through nondimensionalization. The nondimensionalization method has the added advantage of enabling studies that can more broadly apply to various truck configurations. The simulation results indicate that increasing the trailer wheelbase reduces rearward amplification due to the damping effect of the longer wheelbase. A larger momentum ratio due to increased trailer gross weight increases rearward amplification. The detailed models of pneumatic disc and drum brakes in LCVs, including the airflow delay and thermal characteristics, are also developed and are coupled with the articulated vehicle dynamic models. The disc and drum brake braking performance are evaluated and compared in straight-line braking and combined steering and braking at a 150-ft J-turn maneuver. In straight-line braking, the simulation results indicate that disc brakes provide significantly shorter braking distance than drum brakes at highway speeds on a dry road, mainly due to their larger braking torque. On a slippery road surface, however, the greater braking torque causes more frequent wheel lockup and ABS activation at higher speeds, and disc brakes do not provide a substantially shorter braking distance than drum brakes. The simulations also point out that the disc brakes' cooling capacity is higher than the drum brake, with the cooling efficiency heavily dependent on the airflow speed. At higher driving speeds, the airflow accelerates to a turbulent flow and increases the convection efficiency. For braking in-turn maneuvers, at higher entering speeds, disc brakes decelerate the vehicle slightly sooner and then scrub speed faster, resulting in better roll stability when compared with drum brakes.
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    Experimental Evaluation of Wheel-Rail Interaction
    Radmehr, Ahmad (Virginia Tech, 2021-01-14)
    This study provides a detailed experimental evaluation of wheel-rail interaction for railroad vehicles, using the Virginia Tech Federal Railroad Administration (VT-FRA) Roller Rig. Various contact dynamics that emulate field application of railroad wheels on tracks are set up on the rig under precise, highly-controlled and repeatable conditions. For each setup, the longitudinal and lateral traction (creep) forces are measured for different percent creepages, wheel loads, and angles of attack. The tests are performed using quarter-scaled wheels with different profiles, one cylindrical and the other AAR-1B with a 1:20 taper. Beyond the contact forces, the wheel wear and the deposition of worn materials are measured and estimated as a function of time using a micron-precision laser optics measurement device. The change in traction versus amount of worn material at the contact surface is analyzed and related to wheel-rail friction. It is determined that the accumulation of the worn material at the contact surface, which appears as a fine gray powder, acts as a friction modifier that increases friction. The friction (traction) increase occurs asymptotically. Initially, it increases rapidly with time (and worn material accumulation) and eventually reaches a plateau that defines the maximum friction (traction) at a stable rate. It is estimated that the maximum is reached when the running surface is saturated with the worn material. Prior to the saturation, the friction increases directly with an increasing amount of deposited material. The material that accumulates naturally at the surface—hence, referred to as "natural third-body layer"—is estimated to be a ferrous oxide. It has an opposite effect from the Top of Rail (ToR) friction modifiers that are deposited onto the rail surface to reduce friction in a controlled manner. Additionally, the results of the study indicate that longitudinal traction decreases nonlinearly with increasing angle of attack (AoA), while lateral traction increases, also nonlinearly. The AoA is varied from -2.0 to 2.0 degrees, representing a right- and left-hand curve. Lateral traction increases at a high rate with increasing AoA between 0.0 – 0.5 degrees, and increases at a slow rate beyond 0.5 degree. Similarly, longitudinal traction reduces at a high rate for smaller AoA and at a slower rate for larger AoA. For the tapered wheel, an offset in lateral forces is observed for a right-hand curve versus a left-hand curve. The wheel taper generates a lateral traction that is present at all times. In one direction, it adds to the lateral traction due to the AoA, while in the opposite direction, it subtracts from it, resulting in unequal lateral traction for the same AoA in a right-hand versus a left-hand curve. The change in traction with changing wheel load is nearly linear under steady state conditions. Increasing the wheel load increases both longitudinal and lateral tractions linearly. This is attributed to the friction-like behavior of longitudinal and lateral tractions. An attempt is made to measure the contact shape with wheel load using pressure-sensitive films with various degrees of sensitivity. Additionally, the mathematical modeling of the wheel-roller contact in both pure steel-to-steel contact and in the presence of pressure-sensitive films is presented. The modeling results are in good agreement with the measurements, indicating that the pressure-sensitive films have a measurable effect on the shape and contact patch pressure distribution, as compared with steel-to-steel.