2 research outputs found

    Extraction des connaissances en géométrie plane à partir d'énoncés de problèmes

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    RÉSUMÉ: L'émergence actuelle des tuteurs intelligents est appelée à transformer les modes d’enseignement traditionnels. Ces tuteurs intelligents permettent un accompagnement et un soutien personnalisés à chaque élève, et ainsi diminuent la surcharge de travail pour l’enseignant, qui peut alors consacrer plus de temps aux élèves qui ont besoin d’un soutien particulier. Dans ce travail, nous nous intéressons aux tuteurs intelligents qui permettent d’accompagner et d’aider les étudiants de niveau secondaire à résoudre des questions de la géométrie plane. Malheureusement, dans les tuteurs intelligents actuels, et en particulier dans les tuteurs destinés aux utilisateurs qui parlent français, l’extraction automatique des connaissances est laissée aux auteurs du problème. Ces derniers sont amenés à extraire les informations du problème et à les interpréter avant de les saisir manuellement dans l'interface utilisateur qui est sous forme d’une structure d'entrée prédéfinie dans le tuteur (par exemple, une liste des hypothèses et des conclusions). Ce type de tuteur risque de donner des résultats erronés à la suite d’une mauvaise ou une incomplète interprétation. De ce constat nait la motivation de notre recherche, qui est la création d’un extracteur automatique des connaissances à partir d'énoncés de problèmes de géométrie plane écrits en français. Cet extracteur automatisera l’ajout de nouveaux problèmes dans le tuteur. Cet extracteur s’inscrit dans le projet QED-Tutrix, qui a pour but de créer un tuteur intelligent pour la géométrie plane telle qu’enseignée dans le contexte scolaire québécois au niveau secondaire.----------ABSTRACT: The current emergence of smart tutoring systems is called upon to transform traditional teaching methods. These intelligent tutors allow personalized guidance and support for each student, and thus reduce the workload for the teacher, who can then devote more time to students who need special support. In this work, we are interested in intelligent tutors to help high-school students to solve questions of plane geometry. Unfortunately, in today's smart tutors, and especially in tutors for users who speak French, automatic knowledge extraction is left to the authors of the problem. They have to extract information from the problem and interpret it before entering it manually in the user interface, which is in the form of a predefined input structure in the tutor (for example, a list of hypotheses and conclusions). This may cause erroneous results due to a bad or incomplete interpretation. From this observation was born the motivation for our research, which is the creation of an automatic knowledge extractor from plane geometry problems written in French. This extractor will automate the addition of new problems in the tutor. This extractor is part of the QED-Tutrix project, which aims to create an intelligent tutor for plane geometry as taught in the Quebec school context at the secondary level

    Bio-inspired control concepts for elastic rotatory joint drives

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    Annunziata S. Bio-inspired control concepts for elastic rotatory joint drives. Bielefeld: Universität Bielefeld; 2014.Recent research in robotics focuses the attention on the control of compliant actuators to improve safety and to make the interaction with humans more natural. Lightweight construction, real elasticity directly integrated into the joint and control of joint compliance seem to play the most important role for improving safety in human-machine interaction. Humans are intrinsically elastic and the Central Nervous System (CNS) takes advantage of the nonlinear muscle properties to modulate joint stiffness through co-contraction of antagonistic muscles. If alterable compliance in robotic systems is desirable, its introduction can be achieved in two fundamentally different ways. The first way is a technical approach based on the idea of impedance control as formulated by Hogan (1985). The second approach is bioinspired and introduces physiological control mechanisms, muscle models and virtual antagonistic actuation into the control system of a robotics joint drive. Recently, biological models for the control of muscles in vertebrates have been developed (Franklin et al., 2008; Yang et al., 2011). Still, the question remains, how a control algorithm, acting on two or even more muscles, can be implemented in a technical joint. With the objective to implement bio-inspired control strategies on a robotic joint drive, in this thesis, musculoskeletal models, biological parameters and bio-inspired control laws are analyzed and tested. A simplified model of the human elbow joint is used to analyze muscle-like actuation and stiffness properties at the joint. Based on recent results related to how the CNS controls antagonistic muscles, a biological control pattern based on reciprocal activation and co-activation is tested for the control of torque and stiffness at the joint. However, a closer analysis of the musculoskeletal parameters reveals that, despite antagonistic co-activation, domains in the joint range of motion might occur for which stiffness variation is limited (low stiffness variability) or even impossible (stiffness nodes). The first part of this thesis presents novel strategies for simultaneous control of torque and stiffness in a hinge joint actuated by two antagonistic muscle pairs. One strategy handles stiffness nodes by shifting them away from the current joint position and thus regaining stiffness controllability. To prevent domains of low stiffness variation, an optimal biomechanical setup is sought and finally defined which allows for a maximal stiffness variation across a wide angular joint range. Based on this optimal setup, four additional control approaches are designed and tested in simulation which deliver stiffnesses and torques comparable to those obtained in the optimal case. The control approaches combine biologically justified aspects, like reciprocal activation and co-activation, with novel ideas like inverse dynamics model and activation overflow. The second part of the thesis focuses on the design, test and validation of a bio-inspired position and stiffness control strategy for a lightweight, intrinsically elastic, robotics joint drive. Reciprocal activation and co-activation are used here as a starting point to concurrently control stiffness and position (instead of torque). A stability analysis, performed on the human elbow joint model, confirms that the co-activation level (and, as a consequence, the stiffness level) affects the reaction of the joint to external perturbations in terms of oscillations and settling time. To account for the stability aspects and implement further mechanisms found in the CNS of vertebrates, models of the muscle spindles, Golgi tendon organs, alpha-motor neurons and Renshaw cells, are added to the control algorithm. Nevertheless, while in many biological systems, antagonistic muscles generate the movement of the joint, in simple robotic systems, the movement is generated by only one actuator. Therefore, in order to transmit the desired bio-inspired movement to the technical elbow, the sum of all muscle-torques acting on the joint (i.e. the net-torque at the joint), has to be transmitted to the lightweight, inherently elastic, joint drive and controlled. A speed-torque control cascade is designed, implemented and tested on the robotics joint drive. The impedance range of the human elbow joint is evaluated in simulation and compared to the range obtained when the technical joint drive is acting instead of its biological counterpart. The bio-inspired controlled joint drive is able to reach the desired position and modulate joint compliance according to the disturbance like humans do, both in static cases and during movements, while keeping stability
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