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RoboCup SPL 2015 Champion Team Paper

  • Brad Hall
  • Sean HarrisEmail author
  • Bernhard Hengst
  • Roger Liu
  • Kenneth Ng
  • Maurice Pagnucco
  • Luke Pearson
  • Claude Sammut
  • Peter Schmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9513)

Abstract

The Robocup Standard Platform League competition is a highly competitive league, with very little separating the top teams. Winning the competition in consecutive years is particularly challenging as other teams look to counter the tactics and game play of the previous champions. As the reigning champions from 2014, team UNSW Australia was able to overcome this challenge and win the competition for a second consecutive year. Although this success is not only related to developments from this year, this paper focuses on the new innovations and development by team UNSW Australia for the 2015 Robocup Competition. These innovations include white goal detection, whistle detection, foot detection and avoidance, improved path planning and new odometry.

Notes

Acknowledgements

The 2015 team wish to acknowledge the legacy left by previous rUNSWift teams and the considerable financial and administrative support from the School of Computer Science and Engineering, University of New South Wales. We wish to pay tribute to other SPL teams that inspired our innovations in the spirit of friendly competition, especially to Thomas Hamboeck and the Austrian Kangaroos for their insights into whistle detection methods.

References identified as UNSW CSE Robocup reports and other Robocup related references in this paper are available in chronological order from: http://cgi.cse.unsw.edu.au/~robocup/2014ChampionTeamPaperReports/.

References

  1. 1.
    Sushkov, O., Ashar, J., Sammut, C., Teh, B., Ashmore, J., Roy, R., Mei (Jacky), Z., Tsekouras, L., Harris, S., Hengst, B., Hall, B., Liu, R., Pagnucco, M.: RoboCup SPL 2014 champion team paper. In: Bianchi, R.A.C., Akin, H.L., Ramamoorthy, S., Sugiura, K. (eds.) RoboCup 2014. LNCS, vol. 8992, pp. 70–81. Springer, Heidelberg (2015)Google Scholar
  2. 2.
    Hengst, B.: rUNSWift Walk 2014 report, University of New South Wales (2014). http://cgi.cse.unsw.edu.au/~robocup/2014championteampaperreports/20140930-bernhard.hengst-walk2014report.pdf
  3. 3.
    Hengst, B.: Reinforcement learning inspired disturbance rejection and Nao bipedal locomotion. In: 15th IEEE RAS Humanoids Conference, November 2015Google Scholar
  4. 4.
    Hwang, Y., Ahuja, N.: A potential field approach to path planning. IEEE Trans. Robot. Autom. 8(1), 23–32 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Brad Hall
    • 1
  • Sean Harris
    • 1
    Email author
  • Bernhard Hengst
    • 1
  • Roger Liu
    • 1
  • Kenneth Ng
    • 1
  • Maurice Pagnucco
    • 1
  • Luke Pearson
    • 1
  • Claude Sammut
    • 1
  • Peter Schmidt
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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