Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A multi-modal table tennis robot system (2310.19062v2)

Published 29 Oct 2023 in cs.RO and cs.AI

Abstract: In recent years, robotic table tennis has become a popular research challenge for perception and robot control. Here, we present an improved table tennis robot system with high accuracy vision detection and fast robot reaction. Based on previous work, our system contains a KUKA robot arm with 6 DOF, with four frame-based cameras and two additional event-based cameras. We developed a novel calibration approach to calibrate this multimodal perception system. For table tennis, spin estimation is crucial. Therefore, we introduced a novel, and more accurate spin estimation approach. Finally, we show how combining the output of an event-based camera and a Spiking Neural Network (SNN) can be used for accurate ball detection.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. A table tennis robot system using an industrial KUKA robot arm. In Lecture Notes in Computer Science, pages 33–45. Springer International Publishing, 2019. doi:10.1007/978-3-030-12939-2_3. URL https://doi.org/10.1007/978-3-030-12939-2_3.
  2. Robotic table tennis: A case study into a high speed learning system. In Robotics: Science and Systems XIX. Robotics: Science and Systems Foundation, July 2023. doi:10.15607/rss.2023.xix.006. URL https://doi.org/10.15607/rss.2023.xix.006.
  3. Reliable real-time ball tracking for robot table tennis. Robotics, 8(4):90, Oct. 2019. doi:10.3390/robotics8040090. URL https://doi.org/10.3390/robotics8040090.
  4. Learning high speed precision table tennis on a physical robot. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, Oct. 2022. doi:10.1109/iros47612.2022.9982205. URL https://doi.org/10.1109/iros47612.2022.9982205.
  5. J. Billingsley. Robot ping pong. Practical Computing, 6(5), 1983.
  6. Trajectory prediction of spinning ball for ping-pong player robot. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Sept. 2011. doi:10.1109/iros.2011.6095044. URL https://doi.org/10.1109/iros.2011.6095044.
  7. Impedance control and its effects on a humanoid robot playing table tennis. International Journal of Advanced Robotic Systems, 9(5):178, Nov. 2012. doi:10.5772/51924. URL https://doi.org/10.5772/51924.
  8. Learning to play table tennis from scratch using muscular robots. IEEE Transactions on Robotics, 38(6):3850–3860, Dec. 2022. doi:10.1109/tro.2022.3176207. URL https://doi.org/10.1109/tro.2022.3176207.
  9. Learning to select and generalize striking movements in robot table tennis. The International Journal of Robotics Research, 32(3):263–279, Jan. 2013. doi:10.1177/0278364912472380. URL https://doi.org/10.1177/0278364912472380.
  10. Ball tracking and trajectory prediction for table-tennis robots. Sensors, 20(2):333, Jan. 2020. doi:10.3390/s20020333. URL https://doi.org/10.3390/s20020333.
  11. Robotic table tennis with model-free reinforcement learning. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, Oct. 2020. doi:10.1109/iros45743.2020.9341191. URL https://doi.org/10.1109/iros45743.2020.9341191.
  12. J. Tebbe. Adaptive robot systems in highly dynamic environments: A table tennis robot. PhD thesis, University of Tübingen, 2021.
  13. Ping-pong robotics with high-speed vision system. In 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, Dec. 2012. doi:10.1109/icarcv.2012.6485142. URL https://doi.org/10.1109/icarcv.2012.6485142.
  14. Spin detection in robotic table tennis. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2020. doi:10.1109/icra40945.2020.9196536. URL https://doi.org/10.1109/icra40945.2020.9196536.
  15. Measuring ball spin by image registration. Proc. 10th Frontiers of Computer Vision, pages 269–274, 2004.
  16. Pitching a baseball. ACM Transactions on Graphics, 23(3):540–547, Aug. 2004. doi:10.1145/1015706.1015758. URL https://doi.org/10.1145/1015706.1015758.
  17. A study on spin-rate measurement using a uniquely marked moving ball. In 2009 ICCAS-SICE, pages 3439–3442, 2009.
  18. A. Szep. Measuring ball spin in monocular video. In Proc. 16th Comput. Vis. Winter Workshop, pages 83–89. Citeseer, 2011.
  19. Estimating the spin of a table tennis ball using inverse compositional image alignment. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Mar. 2012. doi:10.1109/icassp.2012.6288166. URL https://doi.org/10.1109/icassp.2012.6288166.
  20. J. Glover and L. P. Kaelbling. Tracking the spin on a ping pong ball with the quaternion bingham filter. In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2014. doi:10.1109/icra.2014.6907460. URL https://doi.org/10.1109/icra.2014.6907460.
  21. Real-time spin estimation of ping-pong ball using its natural brand. IEEE Transactions on Instrumentation and Measurement, 64(8):2280–2290, Aug. 2015. doi:10.1109/tim.2014.2385173. URL https://doi.org/10.1109/tim.2014.2385173.
  22. Spindoe: A ball spin estimation method for table tennis robot. In IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS). IEEE, 2023a. doi:10.48550/ARXIV.2303.03879. URL https://arxiv.org/abs/2303.03879.
  23. ewand: A calibration framework for wide baseline frame-based and event-based camera systems, 2023b.
  24. The development of camera calibration methods and models. The Photogrammetric Record, 16(91):51–66, Apr. 1998. doi:10.1111/0031-868x.00113. URL https://doi.org/10.1111/0031-868x.00113.
  25. Z. Zhang. Flexible camera calibration by viewing a plane from unknown orientations. In Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999. doi:10.1109/iccv.1999.791289. URL https://doi.org/10.1109/iccv.1999.791289.
  26. Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes. In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2016. doi:10.1109/icra.2016.7487628. URL https://doi.org/10.1109/icra.2016.7487628.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Andreas Ziegler (15 papers)
  2. Thomas Gossard (10 papers)
  3. Karl Vetter (2 papers)
  4. Jonas Tebbe (9 papers)
  5. Andreas Zell (59 papers)
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now