一种基于融合特征的机器人手腕小齿识别方法研究

随着智能制造系统应用的不断广泛,工业机器人 的需求急剧增加,现有的机器人零 部件产线无法智能判断零件是否合格,导致机器人零部件的测试工序繁杂、合格率不高。针 对上述问题,该文提出一种基于融合特征的机器人手腕小齿识别方法,可以有效识别判断机 器人手腕小齿的轮廓和纹理合格与否。该方法先对机器人手腕小齿进行Zernike矩特征和HOG 特征提取,将高维特征降维后,再通过BP神经网络分类器对目标融合特征进行训练识别。实 验结果表明:基于融合特征的机器人手腕小齿识别特性优于传统单一特征,有更好的识别效 果和鲁棒性。

Research on recognition method of robot wrist pinions based on fusion features

With the wide application of intelligent manufacturing system,the dem and of industrial robots is increasing rapidly.The existing robot parts product ion line can not judge whether the parts are qualified or not,which leads to th e complicated test process and low qualification rate of robot parts.In order t o solve the above problems,this paper proposes a recognition method of robot wr ist pinions based on fusion features,which can effectively identify and judge w hether the contour and texture of the wrist pinions of the robot are qualified o r not.Firstly,the full view image sample database of the target is established by using the simulation software of 3ds max,and then the Zernike moment featur e extraction and hog feature extraction are carried out for the wrist pinions of industrial robot.Thirdly,the dimension of the extracted high-dimensional fea t ures are reduced by different methods.Finally,the fusion features are trained and recognized by BP neural network classifier.The experimental results show th at:the recognition characteristics of robot wrist pinions based on fusion featu res are better than the traditional single feature recognition,and achieve coor dination in accuracy,real-time,stability,robustness and other aspects,and t h e comprehensive recognition effect is good,which is suitable for the qualified judgment of robot parts contour and texture.