Grasp should be selected intelligently to fulfill different stability properties and manipulative requirements. Currently, most grasping approaches consider only pick-and-place tasks without any physical interaction with other objects or the environment, which are common in an industry setting with limited uncertainty. When robots move to our daily-living environment and perform a broad range of tasks in an unstructured environment, all sorts of physical interactions will occur, which will result in random physical interactive wrenches: forces and torques on the tool. In addition, for a tool to perform a required task, certain motions need to occur. We call it "functional tool motion," which represents the innate function of the tool and the nature of the task. Grasping with a robotic hand gives flexibility in "mounting" the tool onto the robotic arm - a different grasp will connect the tool to the robotic arm with a different hand posture, then the inverse kinematics approach will result in a different joint motion of the arm in order to achieve the same functional tool motion. Thus, the grasp and the functional tool motion decide the manipulator's motion, as well as the effort to achieve the motion. Therefore, we propose to establish two objectives to serve the purpose of a grasp: the grasp should maintain a firm grip and withstand interactive wrenches on the tool during the task; and the grasp should enable the manipulator to carry out the task most efficiently with little motion effort, and then search for a grasp to optimize both objectives. For this purpose, two grasp criteria are presented to evaluate the grasp: the task wrench coverage criterion and the task motion effort criterion. The two grasp criteria are used as objective functions to search for the optimal grasp for grasp planning. To reduce the computational complexity of the search in high-dimensional robotic hand configuration space, we propose a novel grasp synthesis approach that integrates two human grasp strategies - grasp type, and thumb placement (position and direction) - into grasp planning. The grasping strategies abstracted from humans should meet two important criteria: they should reflect the demonstrator's intention, and they should be general enough to be used by various robotic hand models. Different abstractions of human grasp constrain the grasp synthesis and narrow down the solutions of grasp generation to different levels. If a strict constraint is imposed, such as defining all contact points of the fingers on the object, the strategy loses flexibility and becomes rarely achievable for a robotic hand with a different kinematic model. Thus, the choice of grasp strategies should balance the learned constraints and required flexibility to accommodate the difference between a human hand and a robotic hand. The human strategies of grasp type and thumb placement have such a balance while conveying important human intents to the robotic grasping. The proposed approach has been thoroughly evaluated both in simulation and on a real robotic system for multiple objects that would be encountered in daily living.

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.

Les travaux de cette thèse portent sur la saisie et la manipulation dextre et ont pour dénominateur commun la robustesse vis-à-vis d'un environnement incertain (méconnaissance de la géométrie de l'objet ou du préhenseur, initialisation imparfaite du système, etc). La mesure de qualité de prise permet d'évaluer la stabilité d'une saisie. Nos travaux proposent une nouvelle mesure de qualité de prise, dont le principe reste dans la continuité des méthodes les plus connues qui consistent à déterminer l'espace des torseurs dynamiques applicables sur l'objet par le préhenseur. Notre mesure cherche à déterminer cet espace quelle que soit l'incertitude qui affecte le système préhenseur/objet. On appelle cet ensemble le Reachable Wrench Space under Uncertainties (RWSU). Deux algorithmes sont proposés afin de déterminer un majorant et un minorant du RWSU. La deuxième contribution concerne l'application d'algorithmes de commande robuste aux incertitudes de modèle pour la manipulation dextre. La première méthode de commande que nous proposons est un retour d'état, permettant de répondre à la consigne de manipulation, auquel on ajoute une action dynamique, permettant de répondre aux contraintes de saisie. Le retour d'état est synthétisé suivant un problème d'optimisation avec contraintes LMI. Les contraintes LMI permettent de définir la réponse dynamique du système bouclé, et d'assurer la robustesse aux incertitudes de modèle. Une seconde méthode de commande est proposée afin d'améliorer les performances de suivi de trajectoire pour ce système MIMO en découplant le mouvement à suivre des mouvements perturbateurs résultant des couplages dynamiques entre les axes. / This thesis deals with grasping and dexterous manipulation with multifingered hands, with the robustness to uncertain environments as a common denominator.The first contribution of the present work is a new measure of the grasp quality, which is used to evaluate the stability of a grasp. In the footsteps of the most known methods which consist in determining the reachable wrench space, our measure aims to evaluate this space whatever the uncertainty which affects the gripper/object system. This new space is called Reachable Wrench Space under Uncertainty (RWSU). Two algorithms are proposed to find respectively an upper and a lower bound of the RWSU.The second contribution concerns the application of robust control algorithms for dexterous manipulation. The first control method is composed of as a state-space feedback, which enables a manipulation task, and of an additional dynamic action, allowing to respect the grasp constraints. The state-space feedback is designed for a robust regional pole placement by the resolution of an optimization problem under LMI constraints. The LMI constraints define the dynamic response of the system in closed loop, and ensure the robustness with respect to model uncertainties. A second control method, based on eigenstructure assignment, is proposed to improve the trajectory tracking for the MIMO system. The eigenstructure assignment decouples the movement of the task from the disturbing movements resulting from the dynamic coupling between the axes.