Les robots ont amélioré les industries, en particulier les systèmes d'assemblage basé sur des conveyors et ils ont le potentiel pour apporter plus de bénéfices: transports; exploration de zones dangereuses, mer profonde et même d'autres planètes; santé et dans la vie courante.Une barrière majeure pour leur évasion des environnements industriels avec des enceintes vers des environnements partagés avec les humains, c'est leur capacité réduite dans les tâches d’interaction physique, inclue la manipulation d'objets.Tandis que la dextérité de la manipulation n'est pas affectée par la cécité dans les humains, elle décroit énormément pour les robots: ils sont limités à des environnements statiques, mais le monde réel est très changeant. Dans cette thèse, nous proposons une approche différente qui considère le contrôle du contact pendant les interaction physiques entre un robot et l'environnement.Néanmoins, les approches actuelles pour l'interaction physique sont pauvres par rapport au numéro de tâches qu'elles peuvent exécuter. Pour permettre aux robots d'exécuter plus de tâches, nous dérivons des caractéristiques tactiles représentant les déformations de la surface molle d'un capteur tactile et nous incorporons ces caractéristiques dans le contrôleur d'un robot à travers des matrices de mapping tactile basées sur les informations tactiles et sur les tâches à développer.Dans notre première contribution, nous montrons comment les algorithmes de traitement d'images peuvent être utilisés pour découvrir la structure tridimensionnelle subjacente du repère de contact entre un objet et une matrice de capteurs de pression avec une surface molle attachée à l’effecteur d'un bras robotique qui interagit avec cet objet. Ces algorithmes obtiennent comme sorties les soi-disant caractéristiques tactiles. Dans notre deuxième contribution, nous avons conçu un contrôleur qui combine ces caractéristiques tactiles avec un contrôleur position-couple du bras robotique.Il permet à l'effecteur du bras déplacer le repère du contact d'une manière désirée à travers la régulation d'une erreur dans ces caractéristiques. Finalement, dans notre dernière contribution,avec l'addition d'une couche de description des tâches, nous avons étendu ce contrôleur pour adresser quatre problèmes communs dans la robotique: exploration, manipulation, reconnaissance et co-manipulation d'objets.Tout au long de cette thèse, nous avons mis l'accent sur le développement d'algorithmes qui marchent pas simplement avec des robots simulés mais aussi avec de robots réels. De cette manière, toutes ces contributions ont été évaluées avec des expériences faites avec au moins un robot réel. En général, ce travail a comme objectif de fournir à la communauté robotique un cadre unifié qui permet aux bras robotique d'être plus dextres et autonomes. Des travaux préliminaires ont été proposés pour étendre ce cadre au développement de tâches qui impliquent un contrôle multi-contact avec des mains robotiques multi-doigts. / Robots have improved industry processes, most recognizably in conveyor-belt assemblysystems, and have the potential to bring even more benefits to our society in transportation,exploration of dangerous zones, deep sea or even other planets, health care and inour everyday life. A major barrier to their escape from fenced industrial areas to environmentsco-shared with humans is their poor skills in physical interaction tasks, includingmanipulation of objects. While the dexterity in manipulation is not affected by the blindnessin humans, it dramatically decreases in robots. With no visual perception, robotoperations are limited to static environments, whereas the real world is a highly variantenvironment.In this thesis, we propose a different approach that considers controlling contact betweena robot and the environment during physical interactions. However, current physicalinteraction control approaches are poor in terms of the range of tasks that can beperformed. To allow robots to perform more tasks, we derive tactile features representingdeformations of the mechanically compliant sensing surface of a tactile sensor andincorporate these features to a robot controller via touch-dependent and task-dependenttactile feature mapping matrices.As a first contribution, we show how image processing algorithms can be used todiscover the underlying three dimensional structure of a contact frame between an objectand an array of pressure sensing elements with a mechanically compliant surfaceattached onto a robot arm’s end-effector interacting with this object. These algorithmsobtain as outputs the so-called tactile features. As a second contribution, we design a tactileservoing controller that combines these tactile features with a position/torque controllerof the robot arm. It allows the end-effector of the arm to steer the contact frame ina desired manner by regulating errors in these features. Finally, as a last contribution, weextend this controller by adding a task description layer to address four common issuesin robotics: exploration, manipulation, recognition, and co-manipulation of objects.Throughout this thesis, we make emphasis on developing algorithms that work notonly with simulated robots but also with real ones. Thus, all these contributions havebeen evaluated in experiments conducted with at least one real robot. In general, thiswork aims to provide the robotics community with a unified framework to that will allowrobot arms to be more dexterous and autonomous. Preliminary works are proposedfor extending this framework to perform tasks that involve multicontact control withmultifingered robot hands.

Many medical devices have been shifting to personal platforms such as smartphones due to its ubiquitous availability, variety of included sensors, robust communication, and user-friendliness. By utilizing smartphones as a medical sensing device should improve the early detection of abnormalities and the long-term monitoring of health conditions. Tissue abnormalities will be detected by touch sensation due to mechanical property changes within the tissue. However, touch sensation is unquantifiable and subjective. We integrate the smartphone with a tactile sensor to build a portable and personalized tissue assessment device based on changes in mechanical properties. The Smartphone Tactile Imaging Probe (STIP) is developed to quantify the mechanical properties of the tissue. The proposed system has a dual-sensing mode: compression-based sensing (STIP-C) and indentation-based sensing (STIP-I). STIP–C is designed to detect and measure the size and hardness of the inclusion. It assesses mechanical property changes caused by the tumor inside the tissue. STIP–I is designed to measure the pitting parameters and viscoelastic properties of the tissue. This system will assess the viscoelasticity changes caused by fluid retention within the tissue. STIP estimates mechanical and viscoelastic behavior changes in the tissue and provides the risk evaluation of an underlying health problem. Breast cancer risk assessment and edema severity level classification are the main applications of STIP. We estimate the breast cancer risk by incorporating the patient’s personal risk value into the STIP-C data associated with the tumor mechanical properties to improve the risk assessment accuracy. To classify the edema severity level, the STIP-I measures the pitting parameters and viscoelastic properties of the tissue. From these parameters, we build a Viscoelastic Pitting Recovery (VPR) model. The model illustrates the changes in tissue viscoelastic behavior associated with the edema severity level. Using the VPR model, we use the thresholding method to classify the edema cases. We also developed customized phantoms representing the different amounts of fluid retention in the tissue. The experimental result found a relationship between the amounts of pitted depth from STIP-I and the fluid amount of a phantom. In this dissertation, we developed and tested a portable tissue mechanical property estimation system. The interchangeable dual-mode STIP sensing probe and risk assessment methods were developed for the breast tumor malignancy and edema severity applications. / Electrical and Computer Engineering

<p dir="ltr">Teletaction, the transmission of tactile feedback or touch, is a crucial aspect in the</p><p dir="ltr">field of teleoperation. High-quality teletaction feedback allows users to remotely manipulate</p><p dir="ltr">objects and increase the quality of the human-machine interface between the operator and</p><p dir="ltr">the robot, making complex manipulation tasks possible. Advances in the field of teletaction</p><p dir="ltr">for teleoperation however, have yet to make full use of the high-resolution 3D data provided</p><p dir="ltr">by modern vision-based tactile sensors. Existing solutions for teletaction lack in one or more</p><p dir="ltr">areas of form or function, such as fidelity or hardware footprint. In this thesis, we showcase</p><p dir="ltr">our research into a low-cost teletaction device for teleoperation that can utilize the real-time</p><p dir="ltr">high-resolution tactile information from vision-based tactile sensors, through both physical</p><p dir="ltr">3D surface reconstruction and shear displacement. We present our device, the Feelit, which</p><p dir="ltr">uses a combination of a pin-based shape display and compliant mechanisms to accomplish</p><p dir="ltr">this task. The pin-based shape display utilizes an array of 24 servomotors with miniature</p><p dir="ltr">Bowden cables, giving the device a resolution of 6x4 pins in a 15x10 mm display footprint.</p><p dir="ltr">Each pin can actuate up to 3 mm in 200 ms, while providing 80 N of force and 3 um of</p><p dir="ltr">depth resolution. Shear displacement and rotation is achieved using a compliant mechanism</p><p dir="ltr">design, allowing a minimum of 1 mm displacement laterally and 10 degrees of rotation. This</p><p dir="ltr">real-time 3D tactile reconstruction is achieved with the use of a vision-based tactile sensor,</p><p dir="ltr">the GelSight, along with an algorithm that samples the depth data and marker tracking to</p><p dir="ltr">generate actuator commands. With our device we perform a series of experiments including</p><p dir="ltr">shape recognition and relative weight identification, showing that our device has the potential</p><p dir="ltr">to expand teletaction capabilities in the teleoperation space.</p>

The hands and feet account for half of the complexity of the musculoskeletal system, while the skin of the hand is specialised with many important structures. Much of the subtlety of the mechanism of the hand lies in the soft tissues, and the tactile and proprioceptive sensitivity depends on the large number of mechanoreceptors embedded in specific structures of the soft tissues. This thesis investigates synthetic materials and manufacturing techniques to enable building robots that reproduce the biomechanics and tactile sensitivity of vertebrates – histomimetic robotics. The material and mechanical anatomy of the hand is reviewed, highlighting difficulty of numerical measurement in soft-tissue anatomy, and the predictive nature of descriptive anatomical knowledge. The biomechanical mechanisms of the hand and their support of sensorimotor control are presented. A palate of materials and layup techniques are identified for emulating ligaments, joint surfaces, tendon networks, sheaths, soft matrices, and dermal structures. A method for thermoplastically drawing fine elastic fibres, with liquid metal amalgam cores, for connecting embedded sensors is demonstrated. The performance requirements of skeletal muscles are identified. Two classes of muscle-like bulk MEMS electrostatic actuators are shown theoretically to be capable of meeting these requirements. Means to manufacture them, and their additional application as mechanoreceptors are described. A novel machine perception algorithm is outlined as a solution to the problem of measuring soft tissue anatomy, CAD/CAE/CNC for layup of histomimetic robots, and sensory perception by such robots. The results of the work support the view that histomimetic robotics is a viable approach, and identify a number of areas for further investigation including: polymer modification by graft-polymerisation, automated layup tools, and machine perception.