15 research outputs found

    Ping-Pong Robotics with High-Speed Vision System

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    Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the RoDyMan Project

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    The goal of this work is to disseminate the results achieved so far within the RODYMAN project related to planning and control strategies for robotic nonprehensile manipulation. The project aims at advancing the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to future enhance the possibility of employing robots in anthropic environments. The final demonstrator of the RODYMAN project will be an autonomous pizza maker. This article is a milestone to highlight the lessons learned so far and pave the way towards future research directions and critical discussions

    Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the RobDyMan Project

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    International audienceThe goal of this work is to disseminate the results achieved so far within the RODYMAN project related to planning and control strategies for robotic nonprehensile manipulation. The project aims at advancing the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to future enhance the possibility of employing robots in anthropic environments. The final demonstrator of the RODYMAN project will be an autonomous pizza maker. This article is a milestone to highlight the lessons learned so far and pave the way towards future research directions and critical discussions

    Design et développement d’un quadrirotor joueur de tennis de table avec des hélices inclinables

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    RÉSUMÉ Les bras manipulateurs joueurs de tennis de table les plus avancés sont chers et leur configuration requière beaucoup d’espace. On pourrait considérer l’utilisation de robots aériens pour exécuter cette tâche, mais la plupart des avions à décollage et atterrissage verticales (ADAV) ne sont pas suffisamment rapides pour reproduire des mouvements de frappes. L’objectif de la recherche présentée dans ce mémoire est de développer un nouveau type de robot aérien joueur de tennis de table. Un prototype de quadrirotor ayant des hélices inclinables est d’abord considéré pour permettre de suivre des trajectoires agressives. Le robot a besoin d’atteindre des vitesses de 3.5 m/s au point d’impact et de rester suffisamment léger pour être agile. Ensuite, pour obtenir de hautes performances sur ce requis, un contrôleur Itérative Linéaire Quadratique Régulateur (iLQR) qui suit des trajectoires ayant un snap minimum est implémenté. Le contrôle de la boucle interne est délégué à un microcontrôleur PX4 pour le tangage, le lacet et le roulis pour assurer une bonne robustesse et une haute fréquence. Cette approche est testée dans une simulation réaliste et ensuite le framework complet pour cette application est développer sur un ordinateur embarqué. Des résultats expérimentaux ont été obtenus avec des caméras de capture de mouvements donnant la position et le temps d’impact. Cette information est envoyée au quadrirotor par communication sans-fil et la trajectoire est exécutée immédiatement. Au meilleur de nos connaissances, il s’agit du premier robot aérien étant capable de retourner des balles de tennis de table lancées par un humain. Un taux de succès de 40% est obtenu sur les frappes avec le modèle réel du quadrirotor, significativement supérieur à ce qui était possible d’atteindre auparavant avec un quadrirotor.----------ABSTRACT State-of-art table tennis robot manipulators are expensive and their setup require a lot of space. One could consider using aerial robots for this task, but most vertical takeoff and landing (VTOL) vehicles are not fast enough to reproduce hitting motions. The objective of the research presented in this thesis is to develop a novel type of aerial robot tennis table player. A prototype of a quadrotor that uses tilting propellers is first considered to enable the possibility of aggressive trajectory tracking. The system needs to reach speeds up to 3.5 m/s at the position of impact and to remain light enough to be agile. Next, to obtain high performances for this requirement, an Iterative Quadratic Linear Controller (iLQR) method that follows minimum snap planned trajectories is implemented. Inner-loop control is delegated to a PX4 microcontroller for roll, pitch and yaw to ensure good robustness and high frequency. This approach is tested in a realistic simulation and then the complete software for this task is developed on an onboard computer. Experimental results have been conducted with a motion capture system to have the full state estimate of the system. The trajectory of the ball is also estimated by the motion capture system, giving the position and time of impact. This information is then sent to quadrotor wirelessly and the trajectory is executed immediately. To the best of our knowledge, this is the first aerial robot capable to return tennis table balls thrown by a human. Hitting rates of 40% are achieved with the real quadrotor, significantly better than what was possible before for a quadrotor

    Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

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    The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

    A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

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    We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces

    Modeling of human movement for the generation of humanoid robot motion

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    La robotique humanoïde arrive a maturité avec des robots plus rapides et plus précis. Pour faire face à la complexité mécanique, la recherche a commencé à regarder au-delà du cadre habituel de la robotique, vers les sciences de la vie, afin de mieux organiser le contrôle du mouvement. Cette thèse explore le lien entre mouvement humain et le contrôle des systèmes anthropomorphes tels que les robots humanoïdes. Tout d’abord, en utilisant des méthodes classiques de la robotique, telles que l’optimisation, nous étudions les principes qui sont à la base de mouvements répétitifs humains, tels que ceux effectués lorsqu’on joue au yoyo. Nous nous concentrons ensuite sur la locomotion en nous inspirant de résultats en neurosciences qui mettent en évidence le rôle de la tête dans la marche humaine. En développant une interface permettant à un utilisateur de commander la tête du robot, nous proposons une méthode de contrôle du mouvement corps-complet d’un robot humanoïde, incluant la production de pas et permettant au corps de suivre le mouvement de la tête. Cette idée est poursuivie dans l’étude finale dans laquelle nous analysons la locomotion de sujets humains, dirigée vers une cible, afin d’extraire des caractéristiques du mouvement sous forme invariants. En faisant le lien entre la notion “d’invariant” en neurosciences et celle de “tâche cinématique” en robotique humanoïde, nous développons une méthode pour produire une locomotion réaliste pour d’autres systèmes anthropomorphes. Dans ce cas, les résultats sont illustrés sur le robot humanoïde HRP2 du LAAS-CNRS. La contribution générale de cette thèse est de montrer que, bien que la planification de mouvement pour les robots humanoïdes peut être traitée par des méthodes classiques de robotique, la production de mouvements réalistes nécessite de combiner ces méthodes à l’observation systématique et formelle du comportement humain. ABSTRACT : Humanoid robotics is coming of age with faster and more agile robots. To compliment the physical complexity of humanoid robots, the robotics algorithms being developed to derive their motion have also become progressively complex. The work in this thesis spans across two research fields, human neuroscience and humanoid robotics, and brings some ideas from the former to aid the latter. By exploring the anthropological link between the structure of a human and that of a humanoid robot we aim to guide conventional robotics methods like local optimization and task-based inverse kinematics towards more realistic human-like solutions. First, we look at dynamic manipulation of human hand trajectories while playing with a yoyo. By recording human yoyo playing, we identify the control scheme used as well as a detailed dynamic model of the hand-yoyo system. Using optimization this model is then used to implement stable yoyo-playing within the kinematic and dynamic limits of the humanoid HRP-2. The thesis then extends its focus to human and humanoid locomotion. We take inspiration from human neuroscience research on the role of the head in human walking and implement a humanoid robotics analogy to this. By allowing a user to steer the head of a humanoid, we develop a control method to generate deliberative whole-body humanoid motion including stepping, purely as a consequence of the head movement. This idea of understanding locomotion as a consequence of reaching a goal is extended in the final study where we look at human motion in more detail. Here, we aim to draw to a link between “invariants” in neuroscience and “kinematic tasks” in humanoid robotics. We record and extract stereotypical characteristics of human movements during a walking and grasping task. These results are then normalized and generalized such that they can be regenerated for other anthropomorphic figures with different kinematic limits than that of humans. The final experiments show a generalized stack of tasks that can generate realistic walking and grasping motion for the humanoid HRP-2. The general contribution of this thesis is in showing that while motion planning for humanoid robots can be tackled by classical methods of robotics, the production of realistic movements necessitate the combination of these methods with the systematic and formal observation of human behavior

    A novel approach to user controlled ambulation of lower extremity exoskeletons using admittance control paradigm

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    The robotic lower extremity exoskeletons address the ambulatory problems confronting individuals with paraplegia. Paraplegia due to spinal cord injury (SCI) can cause motor deficit to the lower extremities leading to inability to walk. Though wheelchairs provide mobility to the user, they do not provide support to all activities of everyday living to individuals with paraplegia. Current research is addressing the issue of ambulation through the use of wearable exoskeletons that are pre-programmed. There are currently four exoskeletons in the U.S. market: Ekso, Rewalk, REX and Indego. All of the currently available exoskeletons have 2 active Degrees of Freedom (DOF) except for REX which has 5 active DOF. All of them have pre-programmed gait giving the user the ability to initiate a gait but not the ability to control the stride amplitude (height), stride frequency or stride length, and hence restricting users’ ability to navigate across different surfaces and obstacles that are commonly encountered in the community. Most current exoskeletons do not have motors for abduction or adduction to provide users with the option for movement in coronal plane, hence restricting user’s ability to effectively use the exoskeletons. These limitations of currently available pre-programmed exoskeleton models are sought to be overcome by an intuitive, real time user-controlled control mechanism employing admittance control by using hand-trajectory as a surrogate for foot trajectory. Preliminary study included subjects controlling the trajectory of the foot in a virtual environment using their contralateral hand. The study proved that hands could produce trajectories similar to human foot trajectories when provided with haptic and visual feedback. A 10 DOF 1/2 scale biped robot was built to test the control paradigm. The robot has 5 DOF on each leg with 2 DOF at the hip to provide flexion/extension and abduction/adduction, 1 DOF at the knee to provide flexion and 2 DOF at the ankle to provide flexion/extension and inversion/eversion. The control mechanism translates the trajectory of each hand into the trajectory of the ipsilateral foot in real time, thus providing the user with the ability to control each leg in both sagittal and coronal planes using the admittance control paradigm. The efficiency of the control mechanism was evaluated in a study using healthy subjects controlling the robot on a treadmill. A trekking pole was attached to each foot of the biped. The subjects controlled the trajectory of the foot of the biped by applying small forces in the direction of the required movement to the trekking pole through a force sensor. The algorithm converted the forces to Cartesian position of the foot in real time using admittance control; the Cartesian position was converted to joint angles of the hip and knee using inverse kinematics. The kinematics, synchrony and smoothness of the trajectory produced by the biped robot was evaluated at different speeds, with and without obstacles, and compared with typical walking by human subjects on the treadmill. Further, the cognitive load required to control the biped on the treadmill was evaluated and the effect of speed and obstacles with cognitive load on the kinematics, synchrony and smoothness was analyzed
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