392 research outputs found

    Using a 3DOF Parallel Robot and a Spherical Bat to hit a Ping-Pong Ball

    Get PDF
    Playing the game of Ping-Pong is a challenge to human abilities since it requires developing skills, such as fast reaction capabilities, precision of movement and high speed mental responses. These processes include the utilization of seven DOF of the human arm, and translational movements through the legs, torso, and other extremities of the body, which are used for developing different game strategies or simply imposing movements that affect the ball such as spinning movements. Computationally, Ping-Pong requires a huge quantity of joints and visual information to be processed and analysed, something which really represents a challenge for a robot. In addition, in order for a robot to develop the task mechanically, it requires a large and dexterous workspace, and good dynamic capacities. Although there are commercial robots that are able to play Ping-Pong, the game is still an open task, where there are problems to be solved and simplified. All robotic Ping-Pong players cited in the bibliography used at least four DOF to hit the ball. In this paper, a spherical bat mounted on a 3-DOF parallel robot is proposed. The spherical bat is used to drive the trajectory of a Ping-Pong ball.Fil: Trasloheros, Alberto. Universidad Aeronáutica de Querétaro; MéxicoFil: Sebastián, José María. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Torrijos, Jesús. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

    Spin observation and trajectory prediction of a ping-pong ball

    Full text link
    © 2014 IEEE. For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method

    Ping-Pong Robotics with High-Speed Vision System

    Get PDF

    Jointly learning trajectory generation and hitting point prediction in robot table tennis

    Get PDF
    This paper proposes a combined learning framework for a table tennis robot. In a typical robot table tennis setup, a single striking point is predicted for the robot on the basis of the ball's initial state. Subsequently, the desired Cartesian racket state and the desired joint states at the striking time are determined. Finally, robot joint trajectories are generated. Instead of predicting a single striking point, we propose to construct a ball trajectory prediction map, which predicts the ball's entire rebound trajectory using the ball's initial state. We construct as well a robot trajectory generation map, which predicts the robot joint movement pattern and the movement duration using the Cartesian racket trajectories without the need of inverse kinematics, where a correlation function is used to adapt these joint movement parameters according to the ball flight trajectory. With joint movement parameters, we can directly generate joint trajectories. Additionally, we introduce a reinforcement learning approach to modify robot joint trajectories such that the robot can return balls well. We validate this new framework in both the simulated and the real robotic systems and illustrate that a seven degree-of-freedom Barrett WAM robot performs well

    Probabilistic movement modeling for intention inference in human-robot interaction.

    No full text
    Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

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

    Get PDF
    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
    • …
    corecore