Dans ce travail de thèse, nous abordons les problèmes de commande posés en co-manipulation robotique pour des tâches de manutention à travers un point de vue dont nous pensons qu’il n’est pas suffisamment exploité, bien qu’il a recourt à des outils classiques en robotique. Le problème de commande en co-manipulation robotique est souvent abordé par le biais des méthodes de contrôle d’impédance, où l’objectif est d’établir une relation mathématique entre la vitesse linéaire du point d’interaction homme-robot et la force d’interaction appliquée par l’opérateur humain au même point. Cette thèse aborde le problème de co-manipulation robotique pour des tâches de manutention comme un problème de commande optimale sous contrainte. Le point de vue proposé se base sur la mise en œuvre d’un Générateur de Trajectoire Temps-Réel spécifique, combiné à une boucle d’asservissement cinématique. Le générateur de trajectoire est conçu de manière à traduire les intentions de l’opérateur humain en trajectoires idéales que le robot doit suivre ? Il fonctionne comme un automate à deux états dont les transitions sont contrôlées par évènement, en comparant l’amplitude de la force d’interaction à un seuil de force ajustable, afin de permettre à l’opérateur humain de garder l’autorité sur les états de mouvement du robot. Pour assurer une interaction fluide, nous proposons de générer un profil de vitesse colinéaire à la force appliquée au point d’interaction. La boucle d’asservissement est alors utilisée afin de satisfaire les exigences de stabilité et de qualité du suivi de trajectoire tout en garantissant l’assistance une interaction homme-robot sûre. Plusieurs méthodes de synthèse sont appliquées pour concevoir des correcteurs efficaces qui assurent un bon suivi des trajectoires générées. L’ensemble est illustré à travers deux modèles de robot. Le premier est le penducobot, qui correspond à un robot sous-actionné à deux degrés de liberté et évoluant dans le plan. Le deuxième est un robot à deux bras complètement actionné. / In this thesis, we address the co-manipulation control problems for the handling tasks through a viewpoint that we do not think sufficiently explored, even it employs classical tools of robotics. The problem of robotic co-manipulation is often addressed using impedance control based methods where we seek to establish a mathematical relation between the velocity of the human-robot interaction point and the force applied by the human operator at this point. This thesis addresses the problem of co-manipulation for handling tasks seen as a constrained optimal control problem. The proposed point of view relies on the implementation of a specific online trajectory generator (OTG) associated to a kinematic feedback loop. This OTG is designed so as to translate the human operator intentions to ideal trajectories that the robot must follow. It works as an automaton with two states of motion whose transitions are controlled by comparing the magnitude of the force to an adjustable threshold, in order to enable the operator to keep authority over the robot’s states of motion. To ensure the smoothness of the interaction, we propose to generate a velocity profile collinear to the force applied at the interaction point. The feedback control loop is then used to satisfy the requirements of stability and of trajectory tracking to guarantee assistance and operator security. Several methods are used to design efficient controllers that ensure the tracking of the generated trajectory. The overall strategy is illustrated through two mechanical systems. The first is the penducobot which is an underactuated robot. The second is the planar robot with two degrees of freedom fully actuated.

Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications. Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy. Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement. / PHD / The use of interactive or “smart” textiles that have sensing material(s) incorporated into them supports an emerging technology for physical activity assessment called Smart Textile Systems (STSs). STSs are an increasingly useful technology for researchers, athletes, patients, and others. Our aims in the current study were the development and assessment of a new smart undershirt (SUS) that was designed to monitor low-back and shoulder motions, and to evaluate the preferred placement and usability of two STSs. We also assessed the accuracy of two smart garments, smart socks (SSs) and the SUS, both individually and in combination. Accuracy was evaluated in terms of the ability of these systems to distinguish between diverse simulated occupational tasks, normal and abnormal walking patterns, and several typical daily activities. Our investigation indicated that STSs could discriminate between different human activities common in three domains: occupational scenarios, healthcare, and activities of daily life. We also found that both smart garments (i.e., SSs and SUS) provided similar accuracy for activity classification, typically exceeding 97%, and thus there was no clear superiority between these two smart garments. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating and monitoring a range of human activities. Use of this technology in the future may have positive implications for health promotion.