VTechWorks Repository :: Browsing by Author "Ben-Tzvi, Pinhas"

Browsing by Author "Ben-Tzvi, Pinhas"

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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.
Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System
Feng, Shumin; Liu, Yujiong; Pressgrove, Isaac; Ben-Tzvi, Pinhas (MDPI, 2024-05-20)
This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.
Autonomous Cricothyroid Membrane Detection and Manipulation using Neural Networks and Robot Arm for First-Aid Airway Management
Han, Xiaoxue (Virginia Tech, 2020-06-02)
The thesis focuses on applying deep learning and reinforcement learning techniques on human keypoint detection and robot arm manipulation. Inspired by Semi-Autonomous Victim Extraction Robot (SAVER), an autonomous first-aid airway-management robotic system designed to perform Cricothyrotomy on patients is proposed. Perception, decision-making, and control are embedded in the system. In this system, first, the location of the cricothyroid membrane (CTM)-the incision site of Cricothyrotomy- is detected; then, the robot arm is controlled to reach the detected position on a medical manikin. A hybrid neural network (HNNet) that can balance both speed and accuracy is proposed. HNNet is an ensemble-based network architecture that consists of two ensembles: the region proposal ensemble and the keypoint detection ensemble. This architecture can maintain the original high resolution of the input image without heavy computation and can meet the high-precision and real-time requirements at the same time. A dataset containing more than 16,000 images from 13 people, with a clear view of the neck area, and with CTM position labeled by a medical expert was built to train and validate the proposed model. It achieved a success rate of $99.6%$ to detect the position of the CTM with an error of less than 5mm. The robot arm manipulator was trained with the reinforcement learning model to reach the detected location. Finally, the detection neural network and the manipulation process are combined as an integrated system. The system was validated in real-life experiments on a human-sized medical manikin using a Kinect V2 camera and a MICO robot arm manipulator.
Beyond LiDAR for Unmanned Aerial Event-Based Localization in GPS Denied Environments
Mayalu Jr, Alfred Kulua (Virginia Tech, 2021-06-23)
Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment.
Circumferential Three-Dimensional Profiling with Specular Micro-Texture Photometry for Dark Objects
Song, Mengyu (Virginia Tech, 2020-06-26)
This dissertation proposes a novel approach to achieve circumferential three-dimensional (3D) profiling for dark objects by investigating specular micro-texture photometry. A small patch of a target surface in micro-texture level yields different appearance under different illumination. This photometric property can be used to reconstruct the target surface with pixel-level resolution. However, due to the nature of some material, the surface of whom has stronger specular components than diffuse components, making the usage of general microtexture photometry more difficult. On the other hand, without using micro-texture photometry, the conventional circumferential 3D approaches only utilizes the geometric property of the target surface, compared to which, the proposed is able to reconstruct the target surface with finer detail. The original contributions of this dissertation are threefold. To begin with, the specular component in the micro-texture photometry is investigated to propose the pixel-level 3D profiling. The intensities of the same pixel from different images, which are taken under different lighting conditions are different. The specular components are used to recover the surface normal of the corresponding surface patch of the target surface. Consequently, the proposed specular-photometry-based technique produces pixel-wise measurement on surface normal. Furthermore, the conventional circumferential 3D profiling approach is extended with the proposed specular-photometry-based technique. The result of 3D profiling via the conventional approach is sparse due to its nature. On the other hand, the result of 3D profiling from the integration using the surface normal obtained from the proposed specular-photometry-based technique suffers from accumulative error. A new approach is then proposed to use the result from the conventional approach as global constraint, for the purpose of reducing the accumulative error. The proposed approach is able to achieve pixel-resolution globally bounded profiling because of the dense surface normal measurement from the proposed specular-photometry-based technique and the constraints from the conventional approach. Lastly, a system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach. The developed system is not only able to acquire data and but also to provide different lighting conditions for both the specular-photometry-based technique and conventional approach using a digital single-lens reflex camera and different lighting devices. With a step motor to rotate the object for three hundred and sixty degrees, the system is able to achieve circumferential scanning
Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization
Christie, Gordon A. (Virginia Tech, 2017-01-05)
Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.
A Collision Avoidance Method Based on Deep Reinforcement Learning
Feng, Shumin; Sebastian, Bijo; Ben-Tzvi, Pinhas (MDPI, 2021-05-19)
This paper set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor. This research aims to determine whether a deep reinforcement learning-based collision avoidance method is superior to the traditional methods, such as potential field-based methods and dynamic window approach. Besides, the proposed obstacle avoidance method was developed as one of the capabilities to enable each robot in a novel robotic system, namely the Self-reconfigurable and Transformable Omni-Directional Robotic Modules (STORM), to navigate intelligently and safely in an unknown environment. A well-conceived hardware and software architecture with features that enable further expansion and parallel development designed for the ongoing STORM projects is also presented in this work. A virtual STORM module with skid-steer kinematics was simulated in Gazebo to reduce the gap between the simulations and the real-world implementations. Moreover, comparisons among multiple training runs of the neural networks with different parameters related to balance the exploitation and exploration during the training process, as well as tests and experiments conducted in both simulation and real-world, are presented in detail. Directions for future research are also provided in the paper.
Comparison of the Role of Beamwidth in Biological and Engineered Sonar
Todd, Bryan Donald (Virginia Tech, 2017-10-31)
Sonar is an important sensory modality for engineers as well as in nature. In engineering, sonar is the dominating modality for underwater sensing. In biology, it is likely to have been a central factor behind the unprecedented evolutionary success of bats, a highly diverse group that accounts for over 20% of all mammal species. However, it remains unclear to what extent engineered and biosonar follow similar design and operational principles. In the current work, the key sonar design characteristic of beamwidth is examined in technical and biosonar. To this end, beamwidth data has been obtained for 23 engineered sonar systems and from numerical beampattern predictions for 151 emission and reception elements (noseleaves and pinnae) from bat biosonar. Beamwidth data from these sources is compared to the beamwidth of a planar ellipsoidal transducer as a reference. The results show that engineered and biological both obey the basic physical limit on beamwidth as a function of the ratio of aperture size and wavelength. However, beyond that, the beamwidth data revealed very different behaviors between the engineered and the biological sonar systems. Whereas the beamwidths of the technical sonar systems were very close to the planar transducer limit, the biological samples showed a very wide scatter away from this limit. This scatter was as large – if not wider – than what was seen in a small reference data set obtained with random aluminum cones. A possible interpretation of these differences in the variability could be that whereas sonar engineers try to minimize beamwidth subject to constraints on device size, the evolutionary optimization of bat biosonar beampatterns has been directed at other factors that have left beamwidth as a byproduct. Alternatively, the biosonar systems may require beamwidth values that are larger than the physical limit and differ between species and their sensory ecological niches.
Design and Analysis of a Variable Inertia Spatial Robotic Tail for Dynamic Stabilization
Wang, Xinran; Ren, Hailin; Kumar, Anil; Ben-Tzvi, Pinhas (MDPI, 2020-10-25)
This paper presents the design of a four degree-of-freedom (DoF) spatial tail and demonstrates the dynamic stabilization of a bipedal robotic platform through a hardware-in-loop simulation. The proposed tail design features three active revolute joints with an active prismatic joint, the latter of which provides a variable moment of inertia. Real-time experimental results validate the derived mathematical model when compared to simulated reactive moment results, both obtained while executing a pre-determined trajectory. A 4-DoF tail prototype was constructed and the tail dynamics, in terms of reactive force and moments, were validated using a 6-axis load cell. The paper also presents a case study where a zero moment point (ZMP) placement-based trajectory planner, along with a model-based controller, was developed in order for the tail to stabilize a simulated unstable biped robot. The case study also demonstrates the capability of the motion planner and controller in reducing the system’s kinetic energy during periods of instability by maintaining ZMP within the support polygon of the host biped robot. Both experimental and simulation results show an improvement in the tail-generated reactive moments for robot stabilization through the inclusion of prismatic motion while executing complex trajectories.
Design and Control of a Cable-Driven Articulated Modular Snake Robot
Racioppo, Peter Charles (Virginia Tech, 2018-01-30)
This thesis presents the design and control of a cable-actuated mobile snake robot. The goal of this research is to reduce the size of snake robots and improve their locomotive efficiency by simultaneously actuating groups of links to fit optimized curvature profiles. The basic functional unit of the snake is a four-link, single degree of freedom module that bends using an antagonistic cable-routing scheme. Elastic elements in series with the cables and the coupled nature of the mechanism allow each module to detect and automatically respond to obstacles. The mechanical and electrical designs of the bending module are presented, with emphasis on the cable-routing scheme, key optimizations, and the use of series elastic actuation. An approximate expression for the propulsive force generated by a snake as a function of its articulation (i.e. the number of links it contains divided by its body length) is derived and a closed-form approximation for the optimal phase offset between joints to maximize the speed of a snake is obtained by simplifying a previous result. A simplified model of serpentine locomotion that considers the forces acting on a single link as it traverses a sinusoid is presented and compared to a detailed multibody dynamic model. Control strategies for snake robots with coupled joints are developed, along with a feedback linearization of the joint dynamics. Experimental studies of force control, locomotion, and adaptation to obstacles using a fully integrated prototype are presented and compared with simulated results.
Design and Control of a Robotic Exoskeleton Glove Using a Neural Network Based Controller for Grasping Objects
Pradhan, Sarthak (Virginia Tech, 2021-08-17)
Patients suffering from brachial plexus injury or other spinal cord related injuries often lose their hand functionality. They need a device which can help them to perform day to day activities by restoring some form of functionality to their hands. A popular solution to this problem are robotic exoskeletons, mechanical devices that help in actuating the fingers of the patients, enabling them to grasp objects and perform other daily life activities. This thesis presents the design of a novel exoskeleton glove which is controlled by a neural network-based controller. The novel design of the glove consists of rigid double four-bar linkage mechanisms actuated through series elastic actuators (SEAs) by DC motors. It also contains a novel rotary series elastic actuator (RSEA) which uses a torsion spring to measure torque, passive abduction and adduction mechanisms, and an adjustable base. To make the exoskeleton glove grasp objects, it also needs to have a robust controller which can compute forces that needs to be applied through each finger to successfully grasp an object. The neural network is inspired from the way human hands can grasp a wide variety of objects with ease. Fingertip forces were recorded from a normal human grasping objects at different orientations. This data was used to train the neural network with a R2 value of 0.81. Once the grasp is initiated by the user, the neural network takes inputs like orientation, weight, and size of the object to estimate the force required in each of the five digits to grasp an object. These forces are then applied by the motors through the SEA and linkage mechanisms to successfully grasp an object autonomously.
Design and Implementation of Articulated Robotic Tails to Augment the Performance of Reduced Degree-of-Freedom Legged Robots
Saab, Wael (Virginia Tech, 2018-04-24)
This dissertation explores the design, and implementation of articulated robotic tail mechanisms onboard reduced degree-of-freedom (DOF) legged robots to augment performance in terms of stability and maneuverability. Fundamentally, this research is motivated by the question of how to improve the stability and maneuverability of legged robots. The conventional approach to address these challenges is to utilize leg mechanisms that are composed of three or more active DOFs that are controlled simultaneously to provide propulsion, maneuvering, and stabilization. However, animals such as lizards and cheetahs have been observed to utilize their tails to aid in these functionalities. It is hypothesized that by using an articulated tail mechanism to aid in these functionalities onboard a legged robot, the burden on the robot's legs to simultaneously maneuver and stabilize the robot may be reduced. This could allow for simplification of the leg's design and control algorithms. In recent years, significant progress has been accomplished in the field of robotic tail implementation onboard mobile robots. However, the main limitation of this work stems from the proposed tail designs, the majority of which are composed of rigid single-body pendulums that provide a constrained workspace for center-of-mass positioning, an important characteristics for inertial adjustment applications. Inspired by lizards and cheetahs that adjust their body orientation using flexible tail motions, two novel articulated, cable driven, serpentine-like tail mechanisms are proposed. The first is the Roll-Revolute-Revolute Tail which is a 3-DOF mechanism, designed for implementation onboard a quadruped robot, that is capable of forming two mechanically decoupled tail curvatures via an s-shaped cable routing scheme and gear train system. The second is a the Discrete Modular Serpentine Tail, designed for implementation onboard a biped robot, which is a modular two-DOF mechanism that distributes motion amongst links via a multi-diameter pulley. Both tail designs utilize a cable transmission system where cables are routed about circular contoured links that maintain equal antagonistic cable displacements that can produce controlled articulated tail curvatures using a single active-DOF. Furthermore, analysis and experimental results have been presented to demonstrate the effectiveness of an articulated tail's ability to: 1) increase the manifold for center-of-mass positioning, and 2) generate enhanced inertial loading relative to conventionally implemented pendulum-like tails. In order to test the tails ability to augment the performance of legged robots, a novel Robotic Modular Leg (RML) is proposed to construct both a reduced-DOF quadrupedal and bipedal experimental platform. The RML is a modular two-DOF leg mechanism composed of two serially connected four-bar mechanisms that utilizes kinematic constraints to maintain a parallel orientation between it's flat foot and body without the use of an actuated ankle. A passive suspension system integrated into the foot enables the dissipation of impact energy and maintains a stable four point-of-contact support polygon on both flat and uneven terrain. Modeling of the combined legged robotic systems and attached articulated tails has led to the derivation of dynamic formulations that were analyzed to scale articulated tails onboard legged robots to maximize inertial adjustment capabilities resulting from tail motions and design a control scheme for tail-aided maneuvering. The tail prototypes, in conjunction with virtual simulations of the quadruped and biped robot, were used in experiments and simulations to implement and analyze the methods for maneuvering and stabilizing the proposed legged robots. Results successfully demonstrate the tails' ability to augment the performance of reduced-DOF legged robots by enabling comparable walking criteria with respect to conventional legged robots. This research provides a firm foundation for future work involving design and implementation of articulated tails onboard legged robots for enhanced inertial adjustment applications.
Design and Integration of a Form-Fitting General Purpose Robotic Hand Exoskeleton
Refour, Eric Montez (Virginia Tech, 2017-12-06)
This thesis explores the field of robotic hand exoskeletons and their applications. These systems have emerged in popularity over the years, due to their potentials to advance the medical field as assistive and rehabilitation devices, and the field of virtual reality as haptic gloves. Although much progress has been made, hand exoskeletons are faced with several design challenges that are hard to overcome without having some tradeoffs. These challenges include: (1) the size and weight of the system, which can affect both the comfort of wearing it and its portability, (2) the ability to impose natural joint angle relationships among the user's fingers and thumb during grasping motions, (3) safety in terms of limiting the range of motions produce by the system to that of the natural human hand and ensuring the mechanical design does not cause harm or injury to the user during usage, (4) designing a device that is user friendly to use, and (5) the ability to effectively perform grasping motions and provide sensory feedback for the system to be applicable in various application fields. In order to address these common issues of today's state-of-the-art hand exoskeleton systems, this thesis proposes a mechanism design for a novel hand exoskeleton and presents the integration of several prototypes. The proposed hand exoskeleton is designed to assist the user with grasping motions while maintaining a natural coupling relationship among the finger and thumb joints to resemble that of a normal human hand. The mechanism offers the advantage of being small-size and lightweight, making it ideal for prolong usage. Several applications are discussed to highlight the proposed hand exoskeleton functionalities in processing sensory information, such as position and interactive forces.
Design and Integration of a Novel Robotic Leg Mechanism for Dynamic Locomotion at High-Speeds
Kamidi, Vinaykarthik Reddy (Virginia Tech, 2018-01-29)
Existing state-of-the-art legged robots often require complex mechanisms with multi-level controllers and computationally expensive algorithms. Part of this is owed to the multiple degrees of freedom (DOFs) these intricate mechanisms possess and the other is a result of the complex nature of dynamic legged locomotion. The underlying dynamics of this class of non-linear systems must be addressed in order to develop systems that perform natural human/animal-like locomotion. However, there are no stringent rules for the number of DOFs in a system; this is merely a matter of the locomotion requirements of the system. In general, most systems designed for dynamic locomotion consist of multiple actuators per leg to address the balance and locomotion tasks simultaneously. In contrast, this research hypothesizes the decoupling of locomotion and balance by omitting the DOFs whose primary purpose is dynamic disturbance rejection to enable a far simplified mechanical design for the legged system. This thesis presents a novel single DOF mechanism that is topologically arranged to execute a trajectory conducive to dynamic locomotive gaits. To simplify the problem of dynamic balancing, the mechanism is designed to be utilized in a quadrupedal platform in the future. The preliminary design, based upon heuristic link lengths, is presented and subjected to kinematic analysis to evaluate the resulting trajectory. To improve the result and to analyze the effect of key link lengths, sensitivity analysis is then performed. Further, a reference trajectory is established and a parametric optimization over the design space is performed to drive the system to an optimal configuration. The evolved design is identified as the Bio-Inspired One-DOF Leg for Trotting (BOLT). The dynamics of this closed kinematic chain mechanism is then simplified, resulting in a minimal order state space representation. A prototype of the robotic leg was integrated and mounted on a treadmill rig to perform various experiments. Finally, open loop running is implemented on the integrated prototype demonstrating the locomotive performance of BOLT.
Design of a Gravity Compensation Actuator for Arm Assistance
Tang, Chen (Virginia Tech, 2018-02-19)
This thesis presents the design, simulation, and evaluation of a passive, wearable, and human-scale actuator that includes pulleys and uses polymers for energy storage. Repetitive tasks such as packing boxes on an assembly line may require high strength movements of the shoulder, arm, and hand and may result in musculoskeletal disorders. With the objective to offset the weight of the arm and thereby lower the forces on the muscles in the shoulder and arm, this actuator is able to provide gravity compensation for the upper extremities of workers, if used in conjunction with an arm exoskeleton. The actuator is passive, meaning that it does not use motors or sensors, but instead creates a force on a cable that is a function of the displacement of the cable. This thesis details the design of the actuator and the selection of an appropriate polymer for use with the actuator. To determine the best polymer for this application, tests were conducted on nine polymers to ind their average Young's modulus and their hysteresis. A 90A abrasion-resistant polyurethane rubber belt was used in the final design due to its high modulus and low hysteresis. The final actuator design was tested in an Instron machine to validate its performance. During testing, the actuator provided 720N in extension and 530N in retraction, which are roughly 112% and 83% of the torque required to lift a human arm, respectively.
Design, Analysis, Planning, and Control of a Novel Modular Self-Reconfigurable Robotic System
Feng, Shumin (Virginia Tech, 2022-01-11)
This dissertation describes the design, analysis, planning, and control of a self-reconfigurable modular robotic system. The proposed robotic system mainly contains three major types of robotic modules: load carrier, manipulation module, and locomotion module. Each module is capable of navigation and interaction with the environment individually. In addition, the robotic system is proposed to reassemble autonomously into various configurations to perform complex tasks such as humanoid configuration to enable enhanced functionality to reconfigure into a configuration that would enable the system to cross over a ditch. A non-back drivable active docking mechanism with two Degrees of Freedom (DOFs) was designed to fit into the tracked units of the robot modules for achieveing the reconfiguration. The quantity and location of the docking mechanisms are customizable and selectable to satisfy various mission requirements and adapt to different environments. During the reconfiguration process, the target coupling mechanism of each module reconfigurable with each other autonomously. A Lyapunov function-based precision controller was developed to align the target docking mechanisms in a close range and high precision for assembling the robot modules autonomously into other configurations. Additionally, an trajectory optimization algorithm was developed to help the robot determine when to switch the locomotion modes and find the fastest path to the destination with the desired pose.
Design, Development, and Control of an Assistive Robotic Exoskeleton Glove Using Reinforcement Learning-Based Force Planning for Autonomous Grasping
Xu, Wenda (Virginia Tech, 2023-10-11)
This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices have emerged as a promising avenue for providing assistance to individuals with hand disabilities, especially those who may not achieve full recovery through surgical interventions. Nevertheless, prevailing exoskeleton glove systems encounter a multitude of challenges spanning design, control, and human-machine interaction. These challenges have given rise to limitations, such as unwieldy bulkiness, an absence of precise force control algorithms, limited portability, and an imbalance between lightweight construction and the essential functionalities required for everyday activities. To address these challenges, this research undertakes a comprehensive exploration of various dimensions within the exoskeleton glove system domain. This includes the intricate design of the finger linkage mechanism, meticulous kinematic analysis, strategic kinematic synthesis, nuanced dynamic modeling, thorough simulation, and adaptive control. The development of two distinct types of series elastic actuators, coupled with the creation of two diverse exoskeleton glove designs based on differing mechanisms, constitutes a pivotal aspect of this study. For the exoskeleton glove integrated with series elastic actuators, a sophisticated dynamic model is meticulously crafted. This endeavor involves the formulation of a mathematical framework to address backlash and the subsequent mitigation of friction forces. The pursuit of accurate force control culminates in the proposition of a data-driven model-free force predictive control policy, compared with a dynamic model-based force control methodology. Notably, the efficacy of the system is validated through meticulous clinical experiments. Meanwhile, the low-profile exoskeleton glove design with a novel mechanism engages in a further reduction of size and weight. This is achieved through the integration of a rigid coupling hybrid mechanism, yielding pronounced advancements in wearability and comfortability. A deep reinforcement learning approach is adopted for the real-time force planning control policies. A simulation environment is built to train the reinforcement learning agent. In summary, this research endeavors to surmount the constraints imposed by existing exoskeleton glove systems. By virtue of advancing mechanism design, innovating control strategies, enriching perception capabilities, and enhancing wearability, the ultimate goal is to augment the functionality and efficacy of these devices within the realm of assistive applications.
Design, Simulation, and Experimental Validation of a Novel High-Speed Omnidirectional Underwater Propulsion Mechanism
Njaka, Taylor Dean (Virginia Tech, 2021-01-11)
This dissertation explores a novel omnidirectional propulsion mechanism for observation-class underwater vehicles, enabling for operation in extreme, hostile, or otherwise high-speed turbulent environments where unprecedented speed and agility are necessary. With a small overall profile, the mechanism consists of two sets of counter-rotating blades operating at frequencies high enough to dampen vibrational effects on onboard sensors. Each rotor is individually powered to allow for roll control via relative motor effort and attached to a swashplate mechanism, providing quick and powerful manipulation of fluid-flow direction in the hull's coordinate frame without the need to track rotor position. The omnidirectional mechanism exploits properties emerging from its continuous counter-rotating blades to generate near-instantaneous forces and moments in six degrees of freedom (DOF) of considerable magnitude, and is designed to allow each DOF to be controlled independently by one of six decoupled control parameters. The work presented in this dissertation validates the mechanism through physical small-scale experimentation, confirming near-instantaneous reaction time, and aligning with computational fluid dynamic (CFD) results presented for the proposed theorized full-scale implementation. Specifically, it is demonstrated that the mechanism can generate sway thrust at 10-20% surge thrust capacity in both simulation and physical tests. It is also shown that the magnitude of forces and moments generated is directly proportional to motor effort and corresponding commands, in par with theory. Any apparent couplings between different control modes are deeply understood and shown to be trivially accounted for, effectively uncoupling all six control parameters. The design, principles, and bullard-pull simulation of the proposed full-scale mechanism and vehicle implementation are then thoroughly discussed. Kinematic and hydrodynamic analyses of the hull and surrounding fluid forces during different maneuvers are presented, followed by the mechanical design and kinematic analysis of each subsystem. To estimate proposed full-scale performance specifications and UUV turbulence rejection, a full six-DOF maneuvering model is constructed from first principles utilizing CFD and regression techniques. This dissertation thoroughly examines the working principles and performance of a novel omnidirectional propulsion mechanism. With the small-scale model and full scale simulation and analysis, the work presented successfully demonstrates the mechanism can generate nearly instantaneous omnidirectional forces underwater in a controlled manner, with application to high-speed agile vehicles in dynamic environments.
Development of Intelligent Exoskeleton Grasping Through Sensor Fusion and Slip Detection
Lee, Brielle (Virginia Tech, 2018)
This thesis explores the field of hand exoskeleton robotic systems with slip detection and its applications. It presents the design and control of the intelligent sensing and force- feedback exoskeleton robotic (iSAFER) glove to create a system capable of intelligent object grasping initiated by detection of the user’s intentions through motion amplification. Using a combination of sensory feedback streams from the glove, the system has the ability to identify and prevent object slippage, as well as adapting grip geometry to the object properties. The slip detection algorithm provides updated inputs to the force controller to prevent an object from being dropped, while only requiring minimal input from a user who may have varying degrees of functionality in their injured hand. This thesis proposes the use of a high dynamic range, low cost conductive elastomer sensor coupled with a negative force derivative trigger that can be leveraged in order to create a controller that can intelligently respond to slip conditions through state machine architecture, and improve the grasping robustness of the exoskeleton. The mechanical and electrical improvements to the previous design, the sensing and force- feedback exoskeleton robotic (SAFER) glove, are described while details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are also presented. In closing, this thesis concludes with topics for future exploration.
Enhanced Portability and Anti-Frosting Functionality of Cryostats for Synchrotron-Based X-ray Imaging
Lowery, Adam Wallace (Virginia Tech, 2022-08-22)
The intensity of light produced from synchrotrons enables X-ray imaging down to the micron and submicron scale. This high degree of resolution is necessary to study metals in hydrated biological samples, where trace (metal) elements are found in the lowest concentration. Water within these aqueous samples will undergo radiolysis and produce various reactive oxygen species, which degrades the quality of information gathered from the sample during X-ray imaging. Studies have shown that the best way to counter the effects of radiolysis and preserve samples in their metabolic state during X-ray imaging is to keep them cryogenically frozen. We have developed affordable cryostats and novel protocol to not only improve cryo-imaging at current third-generation synchrotrons, but also enable cryo-imaging at existing synchrotrons that have limited accessibility. This dissertation will provide a detailed description of the tasks that were accomplished to contribute to the development cryo-imaging. The first task was the fabrication of a portable cryostage. The cryostage's discreet profile and unique design successfully enabled it to be effortlessly adapted into three beamlines across two different DOE facilities and facilitate multiple imaging modalities, i.e., correlative imaging. With the next task, we explored adding an ice frame about the stage to help reduce the accumulation of frost on the surface of a frozen sample that was explored. The addition of the ice frame significantly improved the imaging of frozen samples, nearly doubling the overall image clarity in comparison to when it was absent. The final task saw the application of a cryostream, in place of a cryostage, to provide a cooled convective flux across the sample for 2D and 3D visualization for cryo X-ray imaging.

Browsing by Author "Ben-Tzvi, Pinhas"

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