作　　者： (周向）; (曹静）; (何涛）;

机构地区： 武汉交通职业学院机电工程学院,湖北武汉430000

出　　处： 《制造技术与机床》 2017年第9期54-59,共6页

摘　　要： 为实现机器人主动识别并抓取预定目标,提出了一种基于形状模型的目标识别算法。以轮廓质心为基准,极半径生成目标的形状特征向量,然后以高斯模型完成样本特征模型库的训练。采用动态时间规整DTW(dynamic time warping)解决特征向量元间的匹配问题,并依据惯量力矩(moment of inertia)获取形状的主轴参数,给出了特征向量起始次序的对正方法。实验表明该算法可以在较复杂环境下识别预定目标,不受平移、尺度与旋转几何变化的影响,具有较高的鲁棒性。 A object recognition algorithm based on shape model is proposed to recognize and grasp the target actively for robot. The algorithm takes the contour centroid as the reference,the shape feature vector is generated by polar radius,and then the sample feature model library is trained through Gauss model. Adapting DTW（ dynamic time warping） to solve the problem of feature matching among the elements vector and acquiring spindle of object shape according to the inertia moment（ moment of inertia） principle,so the initial mapping method of feature vector sequence is given. The experimental results show that the proposed algorithm can identify the target in a complex environment,and it is not subject to the influence of the target translation,scale and rotation.

关 键 词： 形状模型 主动识别 特征向量 机器人 动态时间规整