Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T03:21:17.777Z Has data issue: false hasContentIssue false

Design and control of cooperative ball juggling DELTA robots without visual guidance

Published online by Cambridge University Press:  10 July 2015

Zeeshan Shareef*
Affiliation:
Department of Control Engineering and Mechatronics, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany. E-mails: [email protected], [email protected], [email protected]
Viktor Just
Affiliation:
Department of Control Engineering and Mechatronics, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany. E-mails: [email protected], [email protected], [email protected]
Heinrich Teichrieb
Affiliation:
Department of Control Engineering and Mechatronics, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany. E-mails: [email protected], [email protected], [email protected]
Ansgar Trächtler
Affiliation:
Department of Control Engineering and Mechatronics, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

Cooperative ball juggling is one of the most difficult tasks when performed through autonomous robots. States of the ball (position and velocity) play a vital role for the stability and duration of a long rally. Cameras are normally used in ball juggling to calculate these parameters, the use of which is not only computationally expensive but also requires a lot of hardware to determine. In this paper, we propose a control loop for cooperative ball juggling using parallel DELTA robots without visual guidance. In contrast to using a visual system for ball states feedback, an observer based on the reflection laws is designed to calculate the continuous position and velocity of the ball during juggling. Besides the conventional controller blocks, the proposed control loop consists of the ball prediction and the plate striking movement generation blocks. Two controllers are designed for the stability and tracking of variable reference height of the ball during juggling: One controller calculates the velocity of the striking plate to achieve the reference height of the ball during juggling and the second controls the actuator angles. A simulation study and hardware experiments show applicability of the designed observer and validation of the proposed control loop.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Buhler, M. and Koditschek, D. E., “From Stable to Chaotic Juggling: Theory, Simulation, and Experiments,” Proceedings of International Conference on Robotics and Automation, vol. 3 (1990) pp. 1976–1981.Google Scholar
2. Müllhing, K., Kober, J. and Peters, J., “A Biomimetic Approach Robot to Table Tennis,” Proceedings of International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 1921–1926.Google Scholar
3. Rapp, H. H., “A Ping-Pong Ball Catching and Juggling Robot: A Real-Time Framework for Vision Guided Acting of an Industrial Robot Arm,” Proceedings of 5th International Conference on Automations, Robotics and Applications (2011) pp. 430–435.Google Scholar
4. Sun, L., Liu, J., Wang, Y., Zhou, L., Yang, Q. and He, S., “Ball's Flight Trajectory Prediction for Table-Tennis Game by Humanoid Robot,” Proceedings of International Conference on Robotics and Biomimetics (2009) pp. 2379–2384.Google Scholar
5. Nakashima, A., Sugiyama, Y. and Hayakawa, Y., “Paddle Juggling by Robot Manipulator with Visual Servo,” Proceedings of 9th International Conference on Control, Automation, Robotics and Vision (2006) pp. 1–6.Google Scholar
6. Zhang, Z. and Xu, D., “Design of High-Speed Vision System and Algorithms Based on Distributed Parallel Processing Architecture for Target Tracking,” Proceedings of 7th Asian Control Conference (2009) 1638–1643.Google Scholar
7. Heikkila, J. and Silven, O., “A Four-Step Camera Calibration Procedure with Implicit Image Correction,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1997) pp. 1106–1112.Google Scholar
8. Teachabarikiti, K., Chalidabhongse, T. H. and Thammano, A., “Players Tracking and Ball Detection for an Automatic Tennis Video Annotation,” Proceedings of 11th International Conference on Control Automation Robotics and Vision (ICARCV) (2010) pp. 2461–2494.Google Scholar
9. Forte, D. and Srivastava, A., “Adaptable Architectures for Distributed Visual Target Tracking,” Proceedings of 29th International Conference on Computer Design (ICCD) (2011) pp. 339–345.Google Scholar
10. Liu, S., Papakonstantinou, A., Wang, H. and Chen, D., “Real-Time Object Tracking System on FPGAs,” Proceedings of Symposium on Application Acceleratorsin High-Performance Computing (SAAHPC) (2011) pp. 1–7.Google Scholar
11. Shareef, Z., Just, V., Teichrieb, H., Lankiet, C. and Trächtler, A. “Dynamical Model of Ball Juggling DELTA Robots using Reflection Laws,” Proceedings of 16th International Conference on Advanced Robotics (2013) pp. 1–8.Google Scholar
12. Shareef, Z., Just, V., Teichrieb, H., Lankiet, C. and Trächtler, A. “Design and Control of a Vertical Ball Juggling Delta Robot without Visual Guidance,” Proceedings of 13th International Conference on Intelligent Autonomous Systems (2014) 1–8.Google Scholar
13. Huang, Y., Xu, D., Tan, M. and Su, H., “Trajectory Prediction of Spinning Ball for Ping-Pong Player Robot,” Proceedings of International Conference on Intelligent Robots and Systems (2011) pp. 3434–3439.Google Scholar
14. Nakashima, A., Ogawa, Y., Kobayashi, Y. and Hayakawa, Y., “Modelling of Rebound Phenomenon of a Rigid Ball with Friction and Elastic Effects,” Proceedings of American Control Conference (2010) pp. 1410–1415.Google Scholar
15. Zhang, Z., Xu, D. and Yang, P., “Rebound Model of Table Tennis Ball for Trajectory Prediction,” Proceedings of International Conference on Robotics and Biomimetics (2010) pp. 376–380.Google Scholar
16. Sanfelice, R. G., Teel, A. R. and Sepulchre, R., “A Hybrid Systems Approach to Trajectory Tracking Control for Juggling System,” Proceedings 46th IEEE Conference on Decision and Control (2007) pp. 5282–5287.Google Scholar
17. Buehler, M., Koditschek, D. E. and Kindlmann, P. J., “A Simple Juggling Robot: Theory and Experimentation,” Proceedings of 1st International Symposium on Experimental Robotics (1989) pp. 36–73.Google Scholar
18. Buehler, M., Koditschek, D. E. and Kindlmann, P. J., “Planning and control of robotic juggling and catching tasks,” Int. J. Robot. Res. 13 (2), 101118 (1994).CrossRefGoogle Scholar
19. Buehler, M., Koditschek, D. E. and Kindlmann, P. J., “A Family of Robot Control Strategies for Intermittent Dynamical Environments,” Proceedings of International Conference on Robotics and Automation, vol. 3 (1989) pp. 1296–1301.Google Scholar
20. Buehler, M., Koditschek, D. E. and Kindlmann, P. J., “A One Degree of Freedom Juggler in a Two Degree of Freedom Environment,” Proceedings of International Workshop on Intelligent Robotics (1988) pp. 91–97.Google Scholar
21. Nakashima, A., Sugiyama, Y. and Hayakawa, Y., “Paddle Juggling of One Ball by Robot Manipulator with Visual Servo,” Proceedings of International Conference on Control, Automation, Robotics and Vision (ICARCV) (2006) pp. 1–6.Google Scholar
22. Ronsse, R., Lefevre, P. and Sepulchre, R., “Sensorless stabilization of bounce juggling,” IEEE Trans. Robot. 22 (1), 147159 (2006).Google Scholar
23. Ronsse, R., Lefevre, P. and Sepulchre, R., “Rhythmic feedback control of a blind planar juggler,” IEEE Trans. Robot. 23 (4), 790802.Google Scholar
24. Reist, P. and D'Andrea, R., “Bouncing an Unconstrained Ball in Three Dimensions with a Blind Juggling Robot,” Proceedings of International Conference on Robotics and Automation (ICRA) (2009) pp. 1774–1781.Google Scholar
25. Reist, P. and D'Andrea, R., “Design of the Pendulum Juggler,” Proceedings of International Conference on Robotics and Automation (ICRA) (2011) pp. 5154–5159.Google Scholar
26. Schaal, S. and Atkeson, C. G., “Open Loop Stable Control Strategies for Robot Juggling,” Proceedings of International Conference on Robotics and Automation (ICRA), vol. 3 (1993) pp. 913–918.Google Scholar
27. Rizzi, A. A., Whitcomb, L. L. and Koditschek, D. E., “Distributed real time control of a spatial robot juggler,” IEEE Comput. 25 (5), 1224 (1992).CrossRefGoogle Scholar
28. Rizzi, A. A. and Koditschek, D. E., “Progress in Spatial Robot Juggling,” Proceedings of International Conference on Robotics and Automation (ICRA), vol. 1 (1992) 775–780.Google Scholar
29. Rizzi, A. A. and Koditschek, D. E., “Preliminary Experiments in Spatial Robot Juggling,” Proceedings of 2nd International Symposium on Experimental Robotics (1993) pp. 282–298.Google Scholar
30. Buehler, M., Robotic Tasks with Intermittent Dynamics Ph.D. Thesis (New Haven, US: Yale University, Electrical Engineering) (1990).Google Scholar
31. Lehtihet, H. E. and Miller, B. N., “Numerical study of a billiard in a gravitational field,” Physica D: Nonlinear Phenom. 21 (1), 93104 (1986).Google Scholar
32. Furusato, M., Hanawa, D. and Oguchi, K., “Motion Analysis of Devil Sticking by Motion Capture System,” Proceedings of Conference on Biomedical Engineering and Sciences (IECBES) (2012) pp. 134–139.Google Scholar
33. Ohata, R., Kawaida, Y., Nakaura, S. and Sampei, M., “Feedback Control Experiment of Enduring Rotary Motion of Devil Stick,” Proceedings of SICE Annual Conference, vol. 2 (2003) pp. 1826–1831.Google Scholar
34. Nakamura, K., Nakaura, S. and Sampei, M., “Enduring Rotary Motion Experiment of Devil Stick by General-Purpose Manipulator,” Motion and Vibration Control (2009) pp. 241–251.Google Scholar
35. Dmitriev, A. L., “Inequality of the coefficients of restitution for vertical and horizontal quasielastic impacts of a ball against a massive plate,” Int. Appl. Mech. 38 (6), 747749 (2002).Google Scholar
36. Cross, R., “Measurements of the horizontal coefficient of restitution for a superball and a tennis ball,” Am. J. Phys. 70 (5), 482489 (2002).Google Scholar
37. Clavel, R., “DELTA, a Fast Parallel Robot,” Proceedings of the 18th International Symposium on Industrial Robots (1988) pp. 91–100.Google Scholar
38. D4-500 Delta Robot, Codian Robotics (Netherlands). Available at: www.codian-robotics.com/en/robotics/d4_robots (2014).Google Scholar
39. TwinCAT3 | eXtenden Automation (XA), BECKHOFF New Automation Technology. Available at: www.beckhoff.com/twincat3/ (2014).Google Scholar
40. Pllatinum Force Sensors, PCB Piezotronics. Available at: www.pcb.com/Platinum/force_sensors (2014).Google Scholar

Shareef supplementary material

Shareef supplementary material 1

Download Shareef supplementary material(Video)
Video 7 MB