Le travail présenté s'intéresse à la problématique générale de la mise en oeuvre de mains robotiques à haut niveau de dextérité. Dans ce contexte, nous nous intéressons à la synthèse de prise d'objets en prenant en compte les contraintes propres à la tâche de manipulation visée. La manière dont l'objet est saisi a une importance capitale sur le bon déroulement d'une tâche.Le développement d'algorithmes capables de générer automatiquement des prises optimales implique avant tout la nécessité de définir la notion de prise optimale au regard de la tâche cible. Pour répondre à ce problème, la communauté scientifique propose dans la littérature de nombreux critères de qualité et continue à en développer de nouveaux. Dans cette thèse, nous présentons une extension des travaux proposés avec une étude approfondie de ces critères dans le cadre de la manipulation dextre. Ces critères sont évalués avec une main robotique entièrement actionnée à quatre doigts et seize articulations.Nous quantifions l'efficacité de ces critères dans le cadre de la réalisation de tâches de manipulation fine avec trois types d'objets spécifiques. Deux groupes de critères sont étudiés : d'une part des critères s'appuyant uniquement sur la position des points de contact, et, d'autre part, des critères prenant en compte la cinématique du préhenseur. Cette étude nous a permis de sélectionner un ensemble de critères pertinents pour résoudre le problème de synthèse de prise que nous avons mis en oeuvre dans un processus basé sur une approche évolutionnaire. Cette approche a été validée dans l'environnement de simulation OpenRAVE, puis expérimentalement avec la nouvelle main RoBioSS. / The work presented in this thesis concerns object grasping with dexterous robotic hands. In this work, we are going to focus on the grasp synthesis problem by taking into account the in-hand manipulation task. The initial grasp has a capital role for the successful completion of a given task.In order to develop algorithms which are able to generate automatically correct grasps for a manipulation task, we need to define suitable grasp quality metrics to assess the validity of a grasp. Throughout the years, a large variety of quality measures have been proposed in the literature and researchers keep on developing new ones. However those quality measures are generally developed for simple grippers and for grasping tasks. In this thesis, we will extend the study of selected interesting grasp quality measures for in-hand manipulation tasks. These quality measures will be evaluated on a four finger robotic hand with sixteen fully actuated degrees of freedom.We will assess the chosen quality measures for in-hand manipulation tasks with three different carefully selected type of objects. The quality metrics are classified in two groups, first one focuses exclusively on the location of contact points and the second one considers the kinematics of the robotic hand. The review of these quality measures led us to select the ones meaningful for solving the grasp synthesis problem for in-hand manipulation. The grasping pipeline implemented to generate the correct grasps is based on an evolutionary approach using a mix of the selected quality measures. The proposed approach was tested in the OpenRAVE robotic simulator and also validated experimentally with the new RoBioSS hand.

Because communication has always been difficult for people who are deaf-blind, The Smith-Kettlewell Eye Research Institute (SKERI), in conjunction with the California Polytechnic State University Mechanical Engineering department, has commissioned the design, construction, testing, and programming of a robotic hand capable of performing basic fingerspelling to help bridge the communication gap. The hand parts were modeled using SolidWorks and fabricated using an Objet rapid prototyper. Its fingers are actuated by 11 Maxon motors, and its wrist is actuated by 2 Hitec servo motors. The motors are controlled by Texas Instruments L293D motor driver chips, ATtiny2313 slave microcontroller chips programmed to act as motor controllers, and a master ATmega644p microcontroller. The master controller communicates with a computer over a USB cable to receive sentences typed by a sighted user. The master controller then translates each letter into its corresponding hand gesture in the American Manual Alphabet and instructs each motor controller to move each finger joint into the proper position.

La prensión y manipulación de objetos se ha convertido en un área de gran interés en robótica, especialmente debido al desarrollo de dispositivos de prensión diestra como las manos antropomórficas, que incrementan la flexibilidad y verstilidad de los brazos robóticos, permitiendo así la prensión y manipulación de una gran variedad de objetos con un solo efector final. Esta tesis aborda varios problemas de planificación asociados a la prensión y manipulación de objetos discretos arbitrarios, esto es, objetos de forma arbitraria descritos mediante nubes de puntos o mallas poligonales. La obtención de una prensión con clausura de fuerza (force-closure) y de una prensión localmente óptima se realiza mediante procedimientos de búsqueda orientada basados en razonamientos geométricos en el espacio de prensiones. La medida de calidad de prensión utilizada es la mayor fuerza generalizada de perturbación que la prensión puede resistir, independientemente de la dirección de la perturbación. Sin embargo, las manos mecánicas y dispositivos de prensión reales difícilmente pueden asegurar que los dedos toquen el objeto justamente en los puntos de contacto calculados. Las regiones de contacto independiente (ICRs) se definen de forma tal que un dedo colocado en cada ICR asegura una prensión con clausura de fuerza; estas regiones otorgan robustez frente a errores en el posicionamiento de los dedos. Esta tesis presenta un algoritmo para obtener las ICRs con cualquier número de contactos con o sin fricción sobre la superficie de cualquier objeto tridimensional, asegurando también una calidad mínima controlada. La aproximación planteada genera las ICRs creciéndolas alrededor de los puntos de contacto de una prensión inicial apropiada, por ejemplo una prensión localmente óptima. Este método se extiende también para el cálculo de ICRs cuando varios contactos están fijados de antemano. El concepto de regiones de no prensión (NGRs) se introduce en este trabajo. Las NGRs se definen de forma tal que un dedo colocado en cada NGR siempre produce una prensión sin clausura de fuerza, independientemente de la posición exacta de cada dedo. Las ICRs y NGRs se utilizan para explorar de forma eficiente el espacio de prensiones. Este espacio es construido mediante un método de muestreo que provee muestras de prensiones con o sin clausura de fuerza, que luego se utilizan para calcular ICRs o NGRs respectivamente, y que luego sirven para etiquetar las configuraciones del espacio de prensiones. Se presenta también una secuencia de muestreo determinístico que permite una exploración incremental y uniforme del espacio de prensiones. La generación del espacio de prensiones se utiliza posteriormente para resolver el problema de reprensión (regrasping), esto es, la obtención de trayectorias de las puntas de los dedos sobre la superficie del objeto para cambiar de una prensión inicial a una final sin perder la condición de la clausura de fuerza. La tesis incluye ejemplos de aplicación para ilustrar el desempeño y la relevancia de los algoritmos planteados. / Object grasping and manipulation has become an area of great interest in robotics, specially due to the development of dexterous grasping devices like anthropomorphic hands that increase the flexibility and versatility of the robot arms, allowing the grasping and manipulation of a large variety of objects with a single end effector. This thesis tackles several planning problems associated with grasping and manipulation of arbitrary discrete objects, i.e. objects described with a cloud of points or a polygonal mesh. The computation of a force closure grasp and a locally optimal grasp is tackled using oriented search procedures based on geometric reasoning in the wrench space. The grasp quality measure considered is the largest perturbation wrench that the grasp can resist independently of the perturbation direction. However, real mechanical hands and grasping devices can hardly assure that the fingers will precisely touch the object at the computed contact points. Independent contact regions (ICRs) such that a finger contact in each ICR ensures a force closure grasp, provide robustness in front of finger positioning errors. This thesis presents an approach to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3D object, assuring a controlled minimum grasp quality. The approach generates the ICRs by growing them around the contact points of a given appropriated starting grasp, like for instance a locally optimal grasp. The approach is also extended to compute the ICRs when several contacts are fixed beforehand. The notion of Non-Graspable Regions (NGRs) is introduced in this work, such that a finger contact in each NGR always produces a non-force closure grasp independently of the exact position of each finger. The ICRs and NGRs are used to efficiently explore the grasp space. The grasp space is constructed using a sampling method that provides samples of force closure or non force closure grasps used to compute ICRs or NGRs, respectively, which are used to label the configurations of the grasp space. An efficient deterministic sampling sequence is provided to allow a good incremental and uniform exploration of the grasp space. The generation of the grasp space is then applied to solve the regrasping problem, i.e. to obtain trajectories of the fingertips on the object surface in order to change from an initial to a final grasp without losing the force closure condition. Application examples are included to illustrate the relevance and performance of the proposed approaches.

abstract: Effective tactile sensing in prosthetic and robotic hands is crucial for improving the functionality of such hands and enhancing the user's experience. Thus, improving the range of tactile sensing capabilities is essential for developing versatile artificial hands. Multimodal tactile sensors called BioTacs, which include a hydrophone and a force electrode array, were used to understand how grip force, contact angle, object texture, and slip direction may be encoded in the sensor data. Findings show that slip induced under conditions of high contact angles and grip forces resulted in significant changes in both AC and DC pressure magnitude and rate of change in pressure. Slip induced under conditions of low contact angles and grip forces resulted in significant changes in the rate of change in electrode impedance. Slip in the distal direction of a precision grip caused significant changes in pressure magnitude and rate of change in pressure, while slip in the radial direction of the wrist caused significant changes in the rate of change in electrode impedance. A strong relationship was established between slip direction and the rate of change in ratios of electrode impedance for radial and ulnar slip relative to the wrist. Consequently, establishing multiple thresholds or establishing a multivariate model may be a useful method for detecting and characterizing slip. Detecting slip for low contact angles could be done by monitoring electrode data, while detecting slip for high contact angles could be done by monitoring pressure data. Predicting slip in the distal direction could be done by monitoring pressure data, while predicting slip in the radial and ulnar directions could be done by monitoring electrode data. / Dissertation/Thesis / M.S. Bioengineering 2012