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Improved Goalie Strategy with the Aldebaran Nao humanoid Robots*

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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  1. 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.

  2. Importance of New Strategy • Increase number of goalie saves • Decrease score deficits • “The best offense is a good defense”

  3. 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

  4. 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

  5. Tasks • Increase speed of lateral step • Trajectory localization • Accurate ball tracking • Crab position close, dive to cover space

  6. Current Status • Robot code setup • Understanding the code and different files • Color tables set • Tweaking localization • Working on fixed trajectory

  7. 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

  8. Contribution of the work • Fewer goals for the other team • A better Nao soccer team • More wins for UT Austin Villa!!!

  9. 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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