摘要
在RoboCup仿真2D中,目前的阵型编辑工具fedit2无法从全局和对比的角度去分析球员在整个球场不同区域的位置排列。对此,提出了一种新型阵型分析方法。该方法使用数据挖掘的思想从RoboCup阵型文件中分别提取出球和每个球员的位置数据,结合Delaunay三角网络理论进行建模,通过分析对比得出阵型背后的信息。分析结论应用在球队阵型设计和决策中,并在RoboCup世界杯及中国公开赛上证明了其有效性。
In the RoboCup simulation 2D,the fedit2 formation editing tool cannot be used to analyze the ar-rangement of the players in different regions of the stadium from the overall and comparative perspective. There-fore,we put forward a new analytical method. With the idea of data mining, we extracted the position data of the ball and players from RoboCup formation files. Then ,we modeled them with the Delaunay Triangulation network theory and obtained the background information in the formation through comparative analysis. The results were applied in the team formation design and decision-making and proved to be effective in the RoboCup World Cup and China Open.
出处
《苏州科技学院学报（自然科学版）》
CAS
2016年第2期41-44,50,共5页
Journal of Suzhou University of Science and Technology （Natural Science Edition）
基金
安徽省自然科学研究重大项目(KJ2014ZD05)
