Robotic Ball Catching with an Eye-in-Hand Single-Camera System

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth

Adaptive Robot Systems in Highly Dynamic Environments: A Table Tennis Robot

Hintergrund: Tischtennis bietet ideale Bedingungen, um Kamera-basierte Roboterarme am Limit zu testen. Die besondere Herausforderung liegt in der hohen Geschwindigkeit des Spiels und in der großen Varianz von Spin und Tempo jedes einzelnen Schlages. Die bisherige Forschung mit Tischtennisrobotern beschränkt sich jedoch auf einfache Szenarien, d.h. auf langsame Bälle mit einer geringen Rotation. Forschungsziel: Es soll ein lernfähiger Tischtennisroboter entwickelt werden, der mit dem Spin menschlicher Gegner umgehen kann. Methoden: Das vorgestellte Robotersystem besteht aus sechs Komponenten: Ballpositionserkennung, Ballspinerkennung, Balltrajektorienvorhersage, Schlagparameterbestimmung, Robotertrajektorienplanung und Robotersteuerung. Zuerst wird der Ball mit traditioneller Bildverarbeitung in den Kamerabildern lokalisiert. Mit iterativer Triangulation wird dann seine 3D-Position berechnet. Aus der Kurve der Ballpositionen wird die aktuelle Position und Geschwindigkeit des Balles ermittelt. Für die Spinerkennung werden drei Methoden präsentiert: Die ersten beiden verfolgen die Bewegung des aufgedruckten Ball-Logos auf hochauflösenden Bildern durch Computer Vision bzw. Convolutional Neural Networks. Im dritten Ansatz wird die Flugbahn des Balls unter Berücksichtigung der Magnus-Kraft analysiert. Anhand der Position, der Geschwindigkeit und des Spins des Balls wird die zukünftige Flugbahn berechnet. Dafür wird die physikalische Diffenzialgleichung mit Gravitationskraft, Luftwiderstandskraft und Magnus-Kraft schrittweise gelöst. Mit dem berechneten Zustand des Balls am Schlagpunkt haben wir einen Reinforcement-Learning-Algorithmus trainiert, der bestimmt, mit welchen Schlagparametern der Ball zu treffen ist. Eine passende Robotertrajektorie wird von der Reflexxes-Bibliothek generiert. %Der Roboter wird dann mit einer Frequenz von 250 Hz angesteuert. Ergebnisse: In der quantitativen Auswertung erzielen die einzelnen Komponenten mindestens so gute Ergebnisse wie vergleichbare Tischtennisroboter. Im Hinblick auf das Forschungsziel konnte der Roboter - ein Konterspiel mit einem Menschen führen, mit bis zu 60 Rückschlägen, - unterschiedlichen Spin (Über- und Unterschnitt) retournieren - und mehrere Tischtennisübungen innerhalb von 200 Schlägen erlernen. Schlußfolgerung: Bedeutende algorithmische Neuerungen führen wir in der Spinerkennung und beim Reinforcement Learning von Schlagparametern ein. Dadurch meistert der Roboter anspruchsvollere Spin- und Übungsszenarien als in vergleichbaren Arbeiten.Background: Robotic table tennis systems offer an ideal platform for pushing camera-based robotic manipulation systems to the limit. The unique challenge arises from the fast-paced play and the wide variation in spin and speed between strokes. The range of scenarios under which existing table tennis robots are able to operate is, however, limited, requiring slow play with low rotational velocity of the ball (spin). Research Goal: We aim to develop a table tennis robot system with learning capabilities able to handle spin against a human opponent. Methods: The robot system presented in this thesis consists of six components: ball position detection, ball spin detection, ball trajectory prediction, stroke parameter suggestion, robot trajectory generation, and robot control. For ball detection, the camera images pass through a conventional image processing pipeline. The ball’s 3D positions are determined using iterative triangulation and these are then used to estimate the current ball state (position and velocity). We propose three methods for estimating the spin. The first two methods estimate spin by analyzing the movement of the logo printed on the ball on high-resolution images using either conventional computer vision or convolutional neural networks. The final approach involves analyzing the trajectory of the ball using Magnus force fitting. Once the ball’s position, velocity, and spin are known, the future trajectory is predicted by forward-solving a physical ball model involving gravitational, drag, and Magnus forces. With the predicted ball state at hitting time as state input, we train a reinforcement learning algorithm to suggest the racket state at hitting time (stroke parameter). We use the Reflexxes library to generate a robot trajectory to achieve the suggested racket state. Results: Quantitative evaluation showed that all system components achieve results as good as or better than comparable robots. Regarding the research goal of this thesis, the robot was able to - maintain stable counter-hitting rallies of up to 60 balls with a human player, - return balls with different spin types (topspin and backspin) in the same rally, - learn multiple table tennis drills in just 200 strokes or fewer. Conclusion: Our spin detection system and reinforcement learning-based stroke parameter suggestion introduce significant algorithmic novelties. In contrast to previous work, our robot succeeds in more difficult spin scenarios and drills

Development of Immersive and Interactive Virtual Reality Environment for Two-Player Table Tennis

Although the history of Virtual Reality (VR) is only about half a century old, all kinds of technologies in the VR field are developing rapidly. VR is a computer generated simulation that replaces or augments the real world by various media. In a VR environment, participants have a perception of “presence”, which can be described by the sense of immersion and intuitive interaction. One of the major VR applications is in the field of sports, in which a life-like sports environment is simulated, and the body actions of players can be tracked and represented by using VR tracking and visualisation technology. In the entertainment field, exergaming that merges video game with physical exercise activities by employing tracking or even 3D display technology can be considered as a small scale VR. For the research presented in this thesis, a novel realistic real-time table tennis game combining immersive, interactive and competitive features is developed. The implemented system integrates the InterSense tracking system, SwissRanger 3D camera and a three-wall rear projection stereoscopic screen. The Intersense tracking system is based on ultrasonic and inertia sensing techniques which provide fast and accurate 6-DOF (i.e. six degrees of freedom) tracking information of four trackers. Two trackers are placed on the two players’ heads to provide the players’ viewing positions. The other two trackers are held by players as the racquets. The SwissRanger 3D camera is mounted on top of the screen to capture the player’

Development of immersive and interactive virtual reality environment for two-player table tennis

Although the history of Virtual Reality (VR) is only about half a century old, all kinds of technologies in the VR field are developing rapidly. VR is a computer generated simulation that replaces or augments the real world by various media. In a VR environment, participants have a perception of “presence”, which can be described by the sense of immersion and intuitive interaction. One of the major VR applications is in the field of sports, in which a life-like sports environment is simulated, and the body actions of players can be tracked and represented by using VR tracking and visualisation technology. In the entertainment field, exergaming that merges video game with physical exercise activities by employing tracking or even 3D display technology can be considered as a small scale VR. For the research presented in this thesis, a novel realistic real-time table tennis game combining immersive, interactive and competitive features is developed. The implemented system integrates the InterSense tracking system, SwissRanger 3D camera and a three-wall rear projection stereoscopic screen. The Intersense tracking system is based on ultrasonic and inertia sensing techniques which provide fast and accurate 6-DOF (i.e. six degrees of freedom) tracking information of four trackers. Two trackers are placed on the two players’ heads to provide the players’ viewing positions. The other two trackers are held by players as the racquets. The SwissRanger 3D camera is mounted on top of the screen to capture the player’sEThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Parametric impact characterisation of a solid sports ball, WITH a view to developing a standard core for the GAA Sliotar

The main aim of this research was to characterise the dynamic impact behaviour of the sliotar core. Viscoelastic characterisation of the balls was conducted for a range of impact speeds. Modern polymer balls exhibited strain and strain-rate sensitivity while traditional multi-compositional balls exhibited strain dependency. The non-linear viscoelastic response was defined by two values of stiffness, initial and bulk stiffness. Traditional balls were up to 2.5 times stiffer than the modern types, with this magnitude being rate-dependent. The greater rate of increase of traditional ball stiffness produced a more non-linear COR velocity-dependence compared to modern balls. The dynamic stiffness results demonstrated limited applicability of quasi-static testing and springtheory equations. Analysis of ball deformation behaviour demonstrated that centre-of mass displacement and diameter compression values were not consistently equivalent for all ball types. The contribution of manufacturing conditions to ball performance was investigated by conducting extensive prototyping experiments. Manufacturing parameters of temperature, pressure and material composition were varied to produce a range of balls. Polymer hardness affected stiffness but not energy dissipation, with increased hardness increasing ball stiffness. The nucleating additive influenced ball COR, with increased additive tending to reduce ball COR, but this effect was sensitive to polymer grade. The impact response of the ball was simulated using three mathematical models. The first model was shown to replicate ball behaviour to only a limited degree, despite being used previously with reported success for other ball types. The second model exhibited a reasonable representation of ball impact response that was universally applicable to all tested ball types; however, the accuracy in terms of predicting force-displacement response was not as high as required for broad range implementation. The third model exhibited significantly better accuracy in simulating ball response. The force values generated from this model exhibited up to 95% agreement with experimental data

2015 GREAT Day Program

SUNY Geneseo’s Ninth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1009/thumbnail.jp

Sports Materials

To further improve the level of correlation between these finite element models and lab-simulated bat/ball impacts, the material behavior for these wood species must also be characterized at strain rates comparable to those experienced ..