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UTS Unleashed! RoboCup@Home SSPL Champions 2019

  • Sammy PfeifferEmail author
  • Daniel Ebrahimian
  • Sarita Herse
  • Tran Nhut Le
  • Suwen Leong
  • Bethany Lu
  • Katie Powell
  • Syed Ali Raza
  • Tian Sang
  • Ishan Sawant
  • Meg Tonkin
  • Christine Vinaviles
  • The Duc Vu
  • Qijun Yang
  • Richard Billingsley
  • Jesse Clark
  • Benjamin Johnston
  • Srinivas Madhisetty
  • Neil McLaren
  • Pavlos Peppas
  • Jonathan Vitale
  • Mary-Anne Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11531)

Abstract

This paper summarizes the approaches employed by Team UTS Unleashed! to take First Place in the 2019 RoboCup@Home Social Standard Platform League. First, our system architecture is introduced. Next, our approach to basic skills needed for a strong performance in the competition. We describe several implementations for tests participation. Finally our software development methodology is discussed.

Keywords

RoboCup@Home RoboCup Social Standard Platform League Social robotics UTS Unleashed! 

Notes

Acknowledgements

We want to thank Cecilio Angulo and Bence Magyar for their help on polishing this paper and the Australian Research Council, WiseTech Global, NSW Chief Scientist and Engineer, Commonwealth Bank of Australia and University of Technology Sydney for the support and crucial funding for the team to compete.

References

  1. 1.
    Pandey, A.K., Gelin, R.: A mass-produced sociable humanoid robot: pepper: the first machine of its kind. IEEE Robot. Autom. Mag. 25(3), 40–48 (2018)CrossRefGoogle Scholar
  2. 2.
    Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software 2009 May 12, vol. 3, no. 3.2, p. 5 (2009)Google Scholar
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283 (2016)Google Scholar
  8. 8.
    Paszke, A., Gross, S., Chintala, S., Chanan, G.: Pytorch: tensors and dynamic neural networks in python with strong gpu acceleration, p. 6. Tensors and dynamic neural networks in Python with strong GPU acceleration, PyTorch (2017)Google Scholar
  9. 9.
    Labbé, M., Michaud, F.: RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J. Field Robot. 36(2), 416–446 (2019)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(Jul), 1755–1758 (2009)Google Scholar
  12. 12.
  13. 13.
  14. 14.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008
  15. 15.
    Huggins-Daines, D., Kumar, M., Chan, A., Black, A.W., Ravishankar, M., Rudnicky, A.I.: PocketSphinx: a free, real-time continuous speech recognition system for hand-held devices. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 14 May 2006, vol. 1, p. I. IEEE (2006). https://github.com/cmusphinx/pocketsphinx
  16. 16.
    Google Cloud Speech to Text. https://cloud.google.com/speech-to-text/
  17. 17.
    Valin, J.M., Maxwell, G., Terriberry, T.B., Vos, K.: High-quality, low-delay music coding in the opus codec. arXiv preprint arXiv:1602.04845, 5 February 2016
  18. 18.
    Pütz, S., Simón, J.S., Hertzberg, J.: Move base flex. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3416–3421, 1 October 2018Google Scholar
  19. 19.
    Rösmann, C., Feiten, W., Wösch, T., Hoffmann, F., Bertram, T.: Efficient trajectory optimization using a sparse model. In: Proceedings of IEEE European Conference on Mobile Robots, Spain, Barcelona, pp. 138–143 (2013)Google Scholar
  20. 20.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv (2018)Google Scholar
  21. 21.
  22. 22.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Proceedings of International Conference on Computer Vision (ICCV), December 2015Google Scholar
  23. 23.
  24. 24.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sammy Pfeiffer
    • 1
    Email author
  • Daniel Ebrahimian
    • 1
  • Sarita Herse
    • 1
  • Tran Nhut Le
    • 1
  • Suwen Leong
    • 1
  • Bethany Lu
    • 1
  • Katie Powell
    • 1
  • Syed Ali Raza
    • 1
  • Tian Sang
    • 1
  • Ishan Sawant
    • 1
  • Meg Tonkin
    • 1
  • Christine Vinaviles
    • 1
  • The Duc Vu
    • 1
  • Qijun Yang
    • 1
  • Richard Billingsley
    • 1
  • Jesse Clark
    • 1
  • Benjamin Johnston
    • 1
  • Srinivas Madhisetty
    • 1
  • Neil McLaren
    • 1
  • Pavlos Peppas
    • 1
  • Jonathan Vitale
    • 1
  • Mary-Anne Williams
    • 1
  1. 1.University of Technology SydneyUltimoAustralia

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