Haptic rendering of parametric surfaces using a feedback stabilized extremal distance tracking algorithm

A new extremal distance tracking algorithm is presented for convex parametric curves and surfaces undergoing rigid body motion. The geometric extremization problem is differ-entiated with respect to time to produce a dynamical system that incorporates dependence on both surface shape and rigid body motion. Extremization then takes place by in-tegrating these dynamical equations, but with a feedback controller in place to stabilize the solution. A controller design using feedback linearization is developed that si-multaneously accounts for surface shape and motion while asymptotically achieving (and maintaining) the extremal pair. Collision detection then takes place in a framework fully analogous to that used for multibody simulation. Lo-cal stability results are extended to provide global stability for body shapes composed of pieced-together convex para-metric surface patches using a switching algorithm

Haptic rendering of continuous parametric models

Haptic rendering is the process of computing restoring forces that are required to generate a perception of touch between a user and a virtual environment. The realism of haptic rendering depends mainly on haptic rendering algorithms and the modeling of virtual objects in a virtual environment. Friction and texture rendering also play an important role in increasing the realism of the experience between a user and a virtual environment. The state of the art haptic and friction rendering algorithms in the literature are developed for polygonal models. These approaches can not benefit from the advantages of continuous parametric surfaces such as compact representation, higher order continuity and exact computation of surface normals. In this thesis, a feedback-stabilized closest point tracking based haptic rendering algorithm is extended by introducing a direct friction rendering method for continuous parametric surfaces. Unlike the existing approaches, the proposed friction rendering method is direct and does not rely on the algorithms introduced for polyhedral surfaces. This algorithm implements the stiction model of friction for haptic rendering of parametric surfaces. It can directly operate on parametric models and can handle surfaces with high curvature. Furthermore, the algorithm allows transitions from sticking to sliding and sliding to sticking, as well as surface to surface transitions, without introducing discontinuous force artifacts. The algorithm also allows for tuning of the friction coefficient during the mode transitions to enable rendering of the Stribeck effect. Thanks to its feedback-stabilized core, it is robust against drift and numerical noise. The algorithm is computationally efficient (with respect to time and space); its applicability and effectiveness to simulate friction are verified through simulations and real-time implementations. In particular, the friction rendering algorithm is tested using pre-determined trajectories that demonstrate successful rendering of static friction at a corner, the mode changes from static-to-dynamic and dynamic-to-static friction

Optimal exoskeleton design and effective human-in-the-loop control frameworks for rehabilitation robotics

Attention, since they decrease the cost of repetitive movement therapies, enable quantitative measurement of the patient progress and promise development of more e ective rehabilitation protocols. The goal of this dissertation is to provide systematic frameworks for optimal design of rehabilitation robots and e ective delivery of therapeutic exercises. The design framework is built upon identification and categorization of the design requirements, and satisfaction of them through several design stages. In particular, type selection is performed to ensure imperative design requirements of safety, ergonomy and wearability, optimal dimensional synthesis is undertaken to maximize global kinematic and dynamic performance defined over the singularity-free workspace volume, while workspace optimization is performed to utilize maximum singularity-free device workspace computed via Grassmann line theory. Then, humanin- the-loop controllers that ensure coupled stability of the human-robot system are implemented in the robot task space using appropriate error metrics. The design framework is demonstrated on a forearm-wrist exoskeleton, since forearm and wrist rotations are critical in performing activities of daily living and recovery of these joints is essential for achieving functional independence of patients. In particular, a non-symmetric 3RPS-R mechanism is selected as the underlying kinematics type and the performance improvements due to workspace and multi-criteria optimizations are experimentally characterized as 27 % larger workspace volume, 32 % higher position control bandwidth and 17 % increase in kinematic isotropy when compared to a similar device in the literature. The exoskeleton is also shown to feature high passive back-driveability and accurate sti ness rendering capability, even under open-loop impedance control. Local controllers to accommodate for each stage of rehabilitation therapies are designed for the forearm-wrist exoskeleton in SO(3): trajectory tracking controllers are designed for early stages of rehabilitation when severely injured patients are kept passive, impedance controllers are designed to render virtual tunnels implementing forbidden regions in the device workspace and allowing for haptic interactions with virtual environments, and passive contour tracking controllers are implemented to allow for rehabilitation exercises that emphasize coordination and synchronization of multi degrees-of-freedom movements, while leaving the exact timing along the desired contour to the patient. These local controllers are incorporated into a multi-lateral shared controller architecture, which allows for patients to train with online virtual dynamic tasks in collaboration with a therapist. Utilizing this control architecture not only enables the shift of control authority of each agent so that therapists can guide or evaluate movements of patients or share the control with them, but also enables the implementation of remote and group therapies, as well as remote assessments. The proposed control framework to deliver e ective robotic therapies can ensure active involvement of patients through online modification of the task parameters, while simultaneously guaranteeing their safety. In particular, utilizing passive velocity field control and extending it with a method for online generation of velocity fields for parametric curves, temporal, spatial and assistive aspects of a desired task can be seamlessly modified online, while ensuring passivity with respect to externally applied forces. Through human subject experiments, this control framework is shown to be e ective in delivering evidence-based rehabilitation therapies, providing assistance as-needed, preventing slacking behavior of patients, and delivering repetitive therapies without exact repetition. Lastly, to guide design of e ective rehabilitation treatment protocols, a set of healthy human subject experiments are conducted in order to identify underlying principles of adaptation mechanism of human motor control system. In these catch-trial based experiments, equivalent transfer functions are utilized during execution of rhythmic dynamic tasks. Statistical evidence suggests that i) force feedback is the dominant factor that guides human adaptation while performing fast rhythmic dynamic tasks rather than the visual feedback and ii) as the e ort required to perform the task increases, the rate of adaptation decreases; indicating a fundamental trade-o between task performance and level of force feedback provided

HAPTIC VISUALIZATION USING VISUAL TEXTURE INFORMATION

Haptic enables users to interact and manipulate virtual objects. Although haptic research has influenced many areas yet the inclusion of computer haptic into computer vision, especially content based image retrieval (CBIR), is still few and limited. The purpose of this research is to design and validate a haptic texture search framework that will allow texture retrieval to be done not just visually but also haptically. Hence, this research is addressing the gap between the computer haptic and CBIR fields. In this research, the focus is on cloth textures. The design of the proposed framework involves haptic texture rendering algorithm and query algorithm. The proposed framework integrates computer haptic and content based image retrieval (CBIR) where haptic texture rendering is performed based on extracted cloth data. For the query purposes, the data are characterized and the texture similarity is calculated. Wavelet decomposition is utilized to extract data information from texture data. In searching process, the data are retrieved based on data distribution. The experiments to validate the framework have shown that haptic texture rendering can be performed by employing techniques that involve either a simple waveform or visual texture information. While rendering process was performed instability forces were generated during the rendering process was due to the limitation of the device. In the query process, accuracy is determined by the number of feature vector elements, data extraction, and similarity measurement algorithm. A user testing to validate the framework shows that users’ perception of haptic feedback differs depending on the different type of rendering algorithm. A simple rendering algorithm, i.e. sine wave, produces a more stable force feedback, yet lacks surface details compared to the visual texture information approach

Haptics-based Modeling and Simulation of Micro-Implants Surgery

Ph.DDOCTOR OF PHILOSOPH

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available