Abstract
This paper presents a novel method to optimize the motion of a paddle within a nonprehensile batting task. The proposed approach shows that it is possible to online predict the impact time and the configuration of the paddle, in terms of its linear velocity and orientation, to re-direct a ball towards a desired location, imposing also a desired spin during the free flight. While exploiting the hybrid dynamics of the task during the minimization process, the obtained position and orientation paths are planned by minimizing the acceleration function of the paddle in SE(3). The batting paths are then tracked by a semi-humanoid robot through a closed-loop kinematic inversion. Numerical tests are implemented to compare different metrics to define the optimal impact time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mason, M.T., Lynch, K.M.: Dynamic manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 152–159. Tokyo, J (1993)
Lynch, K.M., Mason, M.T.: Dynamic nonprehensile manipulation: controllability, planning, and experiments. Int. J. Robot. Res. 18(64), 64–92 (1999)
Serra, D.: Robot control for nonprehensile dynamic manipulation tasks. In: International Conference on Informatics in Control, Automation and Robotics, Doctoral Consortium, pp. 3–12. Lisbon, PT (2016)
Serra, D., Satici, A.C., Ruggiero, F., Lippiello, V., Siciliano, B.: An optimal trajectory planner for a robotic batting task: the table tennis example. In: International Conference on Informatics in Control, Automation and Robotics, pp. 90–101. Lisbon, P (2016)
Andersson, R.L.: A Robot Ping-Pong Player: Experiment in Real-Time Intelligent Control. MIT Press, Cambridge (1988)
Andersson, R.L.: Aggressive trajectory generator for a robot ping-pong player. IEEE Control Syst. Mag. 9(2), 15–21 (1989)
Acosta, L., Rodrigo, J.J., Mendez, J., Marichal, G.N., Sigut, M.: Ping-pong player prototype. IEEE Robot. Autom. Mag. 10(4), 44–52 (2003)
Senoo, T., Namiki, A., Ishikawa, M.: Ball control in high-speed batting motion using hybrid trajectory generator. In: IEEE International Conference on Robotics and Automation, pp. 1762–1767. Orlando, FL, USA (2006)
Sun, Y., Xiong, R., Zhu, Q., Wu, J., Chu, J.: Balance motion generation for a humanoid robot playing table tennis. In: IEEE-RAS International Conference on Humanoid Robots, pp. 19–25. Bled, SI (2011)
Liu, C., Hayakawa, Y., Nakashima, A.: Racket control and its experiments for robot playing table tennis. In: IEEE International Conference on Robotics and Biomimetics, pp. 241–246. Guangzhou, CN (2012)
Lai, C.H., Tsay, T.I.J.: Self-learning for a humanoid robotic ping-pong player. Adv. Robot. 25(9–10), 1183–1208 (2011)
Zhang, Y., Xiong, R., Zhao, Y., Chu, J.: An adaptive trajectory prediction method for ping-pong robots. Intelligent Robotics and Applications, pp. 448–459. Springer, Berlin (2012)
Mülling, K., Kober, J., Kroemer, O., Peters, J.: Learning to select and generalize striking movements in robot table tennis. Int. J. Robot. Res. 32(3), 263–279 (2013)
Huang, Y., Xu, D., Tan, M., Su, H.: Adding active learning to LWR for ping-pong playing robot. IEEE Trans. Control Syst. Technol. 21(4), 1489–1494 (2013)
Yanlong, H., Bernhard, S., Jan, P.: Learning optimal striking points for a ping-pong playing robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4587–4592. Hamburg, D (2015)
Huang, Y., Büchler, D., Schölkopf, B., Peters, J.: Jointly learning trajectory generation and hitting point prediction in robot table tennis. In: IEEE-RAS International Conference on Humanoid Robots, pp. 650–655. Cancun, MX (2016)
+8 Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.: Using probabilistic movement primitives for striking movements. In: IEEE-RAS International Conference on Humanoid Robots, pp. 502–508. Cancun, MX (2016)
Müller, M., Lupashin, S., D’Andrea, R.: Quadrocopter ball juggling. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5113–5120. San Francisco, CA, USA (2011)
Silva, R., Melo, F.S., Veloso, M.: Towards table tennis with a quadrotor autonomous learning robot and onboard vision. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 649–655. Hamburg, D (2015)
Wei, D., Guo-Ying, G., Ye, D., Xiangyang, Z., Han, D.: Ball juggling with an under-actuated flying robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 68–73. Hamburg, D (2015)
Nonomura, J., Nakashima, A., Hayakawa, Y.: Analysis of effects of rebounds and aerodynamics for trajectory of table tennis ball. In: IEEE Society of Instrument and Control Engineers Conference, pp. 1567–1572. Taipei, TW (2010)
Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer Science & Business Media, New York (2010)
Nakashima, A., Ito, D., Hayakawa, Y.: An online trajectory planning of struck ball with spin by table tennis robot. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 865–870. Besançon, F (2014)
do Carmo, M.P.: Riemannian Geometry. Mathematics. Birkhäuser, Boston (1992)
Zefran, M., Kumar, V., Croke, C.B.: On the generation of smooth three-dimensional rigid body motions. IEEE Trans. Robot. Autom. 14(4), 576–589 (1998)
Lippiello, V., Ruggiero, F.: 3D monocular robotic ball catching with an iterative trajectory estimation refinement. In: IEEE International Conference on Robotics and Automation, pp. 3950–3955. Saint Paul, MN, USA (2012)
Cigliano, P., Lippiello, V., Ruggiero, F., Siciliano, B.: Robotic ball catching with an eye-in-hand single-camera system. IEEE Trans. Control Syst. Technol. 23(5), 1657–1671 (2015)
Serra, D., Satici, A.C., Ruggiero, F., Lippiello, V., Siciliano, B.: An optimal trajectory planner for a robotic batting task: the table tennis example (2016). [web page] https://youtu.be/GXtBvbUHu5s
Nakashima, A., Ogawa, Y., Kobayashi, Y., Hayakawa, Y.: Modeling of rebound phenomenon of a rigid ball with friction and elastic effects. In: IEEE American Control Conference, pp. 1410–1415. Baltimore, MD, USA (2010)
Lourakis, M.I.A., levmar: Levenberg-Marquardt nonlinear least squares algorithms in C/C++ (2004). [web page] http://www.ics.forth.gr/~lourakis/levmar/. Accessed 31 Jan 2005
Serra, D., Ruggiero, F., Lippiello, V., Siciliano, B.: A Nonlinear least squares approach for nonprehensile dual-hand robotic ball juggling. World Congress of the International Federation of Automatic Control. Tolouse, FR (2017)
Acknowledgements
The research leading to these results has been supported by the RoDyMan project, which has received funding from the European Research Council FP7 Ideas under Advanced Grant agreement number 320992.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Serra, D., Ruggiero, F., Satici, A.C., Lippiello, V., Siciliano, B. (2018). Time-Optimal Paths for a Robotic Batting Task. In: Madani, K., Peaucelle, D., Gusikhin, O. (eds) Informatics in Control, Automation and Robotics . Lecture Notes in Electrical Engineering, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-319-55011-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-55011-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55010-7
Online ISBN: 978-3-319-55011-4
eBook Packages: EngineeringEngineering (R0)