Improved goalie strategy with the aldebaran nao humanoid robots
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Improved Goalie Strategy with the Aldebaran Nao humanoid Robots*. *This research is supported by NSF Grant No. CNS 1005212. Opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of NSF.

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Improved Goalie Strategy with the Aldebaran Nao humanoid Robots*

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Improved goalie strategy with the aldebaran nao humanoid robots

Improved Goalie Strategy with the AldebaranNao humanoid Robots*

*This research is supported by NSF Grant No. CNS 1005212. Opinions,

findings, conclusions, or recommendations expressed in this paper are

those of the author(s) and do not necessarily reflect the views of NSF.


Importance of new strategy

Importance of New Strategy

  • Increase number of goalie saves

  • Decrease score deficits

  • “The best offense is a good defense”


Current strategy

Current Strategy

Summary

Problems

Ineffective Movement to Ball

Diving is slow to recover from

Accurate shots on goal typically score

  • Stands in goal tracking ball

  • Moves toward ball to block

  • Moves to crab position or dives depending on distance to ball


Improved strategy objectives

Improved Strategy Objectives

  • Block primarily by cutting off shot angles

  • Dive as little as possible

  • Keep control of the ball after blocking the shot

  • Keep the goalie inside the penalty box


Tasks

Tasks

  • Increase speed of lateral step

  • Trajectory localization

  • Accurate ball tracking

  • Crab position close, dive to cover space


Current status

Current Status

  • Robot code setup

  • Understanding the code and different files

  • Color tables set

  • Tweaking localization

  • Working on fixed trajectory


  • Relevant work

    Relevant Work

    Localization

    Color Tracking

    Robot keeping track of the ball from a distance

    • Keeping robot on trajectory

    Goalkeeping Strategy

    • Previous improvement of goalie strategy based on the forest algorithm


    Contribution of the work

    Contribution of the work

    • Fewer goals for the other team

    • A better Nao soccer team

    • More wins for UT Austin Villa!!!


    Sources

    Sources

    • [1]H. Shi, W. Li, Z. Yu, and Y. Qi, “Research on Goalkeeper Strategy Based on Random Forests Algorithm in Robot Soccer,” 2009 First International Conference on Information Science and Engineering, 2009, pp. 946-950.

    • [2]M. Sridharan, G. Kuhlmann, and P. Stone, “Practical Vision-Based Monte Carlo Localization on a Legged Robot,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 3366-3371.

    • [3]S. Zhao, B. Liu, Y. Ren, and J. Han, “Color tracking vision system for the autonomous robot,” 2009 9th International Conference on Electronic Measurement & Instruments, Aug. 2009, pp. 3-182-3-185.


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