谢自强, 葛为民, 王肖锋, 刘军, 刘增昌. 发展型机器人实时特征提取方法研究[J]. 机器人, 2017, 39(2): 189-196. DOI: 10.13973/j.cnki.robot.2017.0189
引用本文: 谢自强, 葛为民, 王肖锋, 刘军, 刘增昌. 发展型机器人实时特征提取方法研究[J]. 机器人, 2017, 39(2): 189-196. DOI: 10.13973/j.cnki.robot.2017.0189
XIE Ziqiang, GE Weimin, WANG Xiaofeng, LIU Jun, LIU Zengchang. Real Time Feature Extraction Method of Developmental Robot[J]. ROBOT, 2017, 39(2): 189-196. DOI: 10.13973/j.cnki.robot.2017.0189
Citation: XIE Ziqiang, GE Weimin, WANG Xiaofeng, LIU Jun, LIU Zengchang. Real Time Feature Extraction Method of Developmental Robot[J]. ROBOT, 2017, 39(2): 189-196. DOI: 10.13973/j.cnki.robot.2017.0189

发展型机器人实时特征提取方法研究

Real Time Feature Extraction Method of Developmental Robot

  • 摘要: 针对发展型机器人自主学习过程中特征提取涉及的增量计算和实时性问题,结合已有的CCIPCA(直观无协方差增量主成分分析)和BDPCA(双向主成分分析)算法,提出了一种增量的BDPCA算法.采用了迭代的计算方式,具备增量的计算能力,并且将2维原始图像矩阵直接作为处理对象,有效地降低了计算量,缩短了程序运行时间.以机械臂待抓取的物块作为实验样本,利用支持向量机进行分类,验证上述算法.实验结果证明了该算法的有效性,平均分类率可达90%,算法处理速度大约26帧/秒,基本满足了发展型机器人的实时处理需求.

     

    Abstract: For the incremental computation and real-time problems of the feature extraction in the self-learning process of developmental robot, an incremental BDPCA (bidirectional principal component analysis) algorithm based on CCIPCA (candid covariance-free incremental principal component analysis) and BDPCA algorithms is proposed. The iterative calculation method is also adopted with the incremental computation ability. In the proposed algorithm, the 2-dimensional original image matrix is taken as the processing object directly, which effectively reduces the computation cost and shortens the running time. To verify the proposed algorithm, the support vector machine method is used to classify the building blocks grasped by the manipulator. The experimental results show that the algorithm is effective and can increase the average classification rate to 90%. The processing speed is approximately 26 frames per second, which can meet the real-time processing needs of developmental robots.

     

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