1. Designing Motion Patterns to Increase Effectiveness of the Goal Keeper in Robot Soccer David Seibert Faculty Advisor: Dr. Mohan Sridharan Texas Tech University 2011 NSF Research Experiences for Undergraduates Site Project Abstract RoboCup is an international research initiative with the stated goal of creating a team of humanoid robots to beat the human soccer champion team by the year 2050. The goal keeper plays a central role in robot soccer, especially when the opponents are capable of good perception, ball control and team collaboration. Since the Aldebaran Nao humanoid robots used in the Standard Platform League of RoboCup possess the ability to kick the ball all the way across the six meter- long field, it is essential that the goal keeper be able to track the ball and move quickly to block the goal while maintaining an accurate estimate of its position in the field. This paper describes the development of motion patterns that significantly increase the effectiveness of the goal keeper to block shots on the goal, thereby increasing the excitement of the game. Introduction RoboCup is an annual competition with teams from Universities across the globe. Though the eventual goal consists of complex robot-human interaction, there are still many intermediate goals that must first be achieved. Increased visual perception of the field and objects Robust movement and balancing capabilities Robot-robot interaction in a team environment Artificial Intelligence leading to increased accuracy in play Beyond soccer, the hope is to create robots that can increase quality of life for humans by taking care of simple and complex chores for people who cannot do it themselves, and people who just want some extra help. There are already many robots in everyday life, robot soccer just gives these robots humanoid characteristics and a wider range of capabilities. Methods During play both the players and ball are constantly in motion. In order to keep the processing on the goalie manageable, the goalie simply tracks the ball and itself. This allows the goalie to calculate correct position without much trouble. Increase localization accuracy through decreased movement speed Program position algorithm into the robot based on localization of the ball and itself o Algorithm starts with data of robot coordinates and the ball coordinates o Uses center of a theoretical circle that has both goal posts as points o Calculates the equation of a line connecting ball and center of theoretical circle o Finds the point where the circle and line intersect in front of the goal o If the point is behind or to the side of the goal post, the goalie stands next to the goal post Once the robot calculates the intersection point of the line and circle, the goalie moves there Goalie is always tracking the ball and localizing itself If the ball doesn’t move, the robot stands still Future Enable crab pose and diving to save the ball Create strategy for passing the ball to get it away from the goal Conclusion Slow movement for accurate localization is more effective than quick movement with less accurate localization Simple strategy that is easy to execute is currently most effective Arched motion yields more saves than attacking the ball References: [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. http://www.aldebaran-robotics.com/en/naoeducationhttp://www.aldebaran-robotics.com/en/naoeducation (picture of Nao) *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. Goalie Position Algorithm //finds slope of line connecting ball and center m = s/(r-h) //finds intersection of line and circle center x = ((r^2)/(m^2)+1)^(1/2)+h y = m(x-h) //adjusts intersection to be in front of goal x = -1(x-2(x-h)) y = y(-1) (r,s) = balls position m = slope of line between ball and circle center x,y = where line and circle intersect (robot position) h,[k] = center of theoretical circle center behind goal Cameras Sonar Sensors Ball Robot Goal Robot Trajectory Raw ImageObject RecognitionBlob Image Goal Posts Penalty Box Ball Body characteristics Height~ 58 cm Weight~ 4.3 kg Body typeTechnical plastic Energy ChargerAC 90-230 volts / DC 24 volts Battery capacity~ 90 min. autonomy Degrees of freedom Head2 DOF Arm5 DOF in each arm Pelvis1 DOF Leg5 DOF in each leg Hand1 DOF in each hand Multimedia Speakers2 Loudspeakers Microphones4 Microphones Vision2 CMOS digital cameras Network access Connections type Wi-fi (IEEE 802.11g) Ethernet connection