Abstract
We propose a method of controlling a paddle so as to return the ball to a desired point on the table with specified flight duration. The proposed method consists of the following three input-output maps implemented by means of Locally Weighted Regression (LWR): (1) A map for predicting the impact time of the ball hit by the paddle and the ball position and velocity at that moment according to input vectors describing the state of the incoming ball; (2) A map representing a change in ball velocities before and after impact; and (3) A map giving the relation between the ball velocity just after impact and the landing point and time of the returned ball. We also propose a feed-forward control scheme based on iterative learning control to accurately achieve the stroke movement of the paddle as determined by using these maps.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burridge R, Rizzi A, Koditschek D (1999) Sequential composition of dynamically dextrous robot behaviors. The International Journal of Robotics Research 18(6):534–555
Schmidt R, Lee T (1999) Motor Control and Learning: A Behavioral Emphasis. Human Kinetics
Andersson R (1988) A Robot Ping-Pong Player: Experiment in Real-Time Intelligent Control. The MIT Press
Ramanantsoa M, Duray A (1994) Towards a Stroke Construction Model. The International Journal of Table Tennis Sciences 2(2):97–114
Miyazaki F, Takeuchi M, Matsushima M, Kusano T, Hashimoto T (2002) Realization of table tennis task based on virtual targets. In: Proc. IEEE Int. Conf. on Robotics and Automation. Washington D.C. 3844–3849
Bühler M, Koditschek D, Kindlmann P (1994) Planning and control of a juggling robot. The International Journal of Robotics Research 13(2):101–118
Aboaf E, Drucker S, Atkeson C (1989) Task-level robot Learning: Juggling a tennis ball more accurately. In: Proc. IEEE Int. Conf. on Robotics and Automation. Scottsdale, Arizona 1290–1295
Atkeson C, Moore A, Schaal S (1997) Locally weighted learning. Artificial Intelligence Review 11(1–5):11–73
MacDorman K (1999) Partition nets: An effcient on-line learning algorithm. In: Proc. 9th Int. Conf. on Advanced Robotics, ICAR'99. Tokyo 529–535
Gorinevsky D, Connolly T (1994) Comparison of some neural network and scattered data approximations: The inverse manipulator kinematics example. Neural Computation 6:521–542
Arimoto S, Kawamura S, Miyazaki F (1984) Bettering Operation of Robots by Learning. Journal of Robotic Systems 1(2):123–140
Kawamura S, Fukao N (1994) Interpolations for Input Torque Patterns obtained through learning control. In: Proc. Int. Conf. on Control, Automation, Robotics, and Vision. Singapore 2194–2198
Myers R (1990) Classical and Modern Regression with Applications. Duxbury, New York
Press W, Teukolsky S, Vetterling W, Flannery B (1993) Numerical Recipes in C. Gijutsu Hyouronsha (Japanese Ed.)
Cross R (2002) Measurements of the horizontal coeficient restitution for a super ball and a tennis ball. The American Journal of Physics 70(5):482–489
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this chapter
Cite this chapter
Miyazaki, F., Matsushima, M., Takeuchi, M. (2006). Learning to Dynamically Manipulate: A Table Tennis Robot Controls a Ball and Rallies with a Human Being. In: Kawamura, S., Svinin, M. (eds) Advances in Robot Control. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37347-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-37347-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37346-9
Online ISBN: 978-3-540-37347-6
eBook Packages: EngineeringEngineering (R0)