Humanoid soccer robots need to adapt to the conditions in human soccer games such as detecting a soccer ball that does not have predefined characteristics such as a definite color and that may blend in with the playing field. For such conditions, the problem cannot be solved by classical detection strategies based on a single colorblock. In this study, the ball color is split into a specific color and a shared color. Two rounds of labelling are used to generate a color lookup table. Color-blocks obtained by pixel-level segmentation are used in a marco-pixel clustering method based on a connecting relationship graph to generate several ball candidates. The best ball object is estimated via the membership function by fuzzy logic. Tests show that the method is able to detect unpredefined balls even in a very disturbed environment and at large distances from the robot and is also able to avoid confusion with the border lines and other robots on the field without excessive computing requirements. The calculations can reach a high framerate of 15 frames per second. This strategy provides an efficient detection method using strictly limited computing resources for robot soccer players.
摘要 为适应人类赛场环境, 仿人足球机器人需能够识别未预先定义足球, 此时足球不再具有固定的与场地鲜明对比的颜色, 因而不能通过传统的单色块识别策略解决。该文将足球颜色划分为专有色和共有色两类, 采用两轮颜色标记方法生成颜色查找表; 在图像像素级联通分割获得色块的基础上, 提出基于图连接关系的宏像素聚类算法, 从而得到若干足球识别候选对象; 再利用模糊逻辑方法中的隶属度函数从候选对象中获得最佳足球识别结果。实验表明在不具备高性能计算硬件平台的情况下, 算法能够在存在大量干扰, 远近距离大幅度变化的条件下准确识别出非预先定义的足球, 避免与边线、机器人等对象的混淆, 且达到每秒15帧的计算速率。该算法能够在严格受限的计算能力下达到高效的足球识别能力, 从而为参赛机器人提供了一种崭新策略。