morinson 发表于 2017-9-4 13:28:52

发展型机器人实时特征提取方法研究

发展型机器人实时特征提取方法研究










发展型机器人实时特征提取方法研究
谢自强1, 葛为民1, 王肖锋1, 刘军1, 刘增昌2
1. 天津市先进机电系统设计与智能控制重点实验室, 天津 300384;
2. 中国汽车工业工程有限公司, 天津 300113

Real Time Feature Extraction Method of Developmental Robot
XIE Ziqiang1, GE Weimin1, WANG Xiaofeng1, LIU Jun1, LIU Zengchang2
1. Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System, Tianjin 300384, China;
2. China Automobile Industry Engineering Co., LTD, Tianjin 300113, China







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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.

关键词 : 增量主成分分析,特征提取,发展型机器人   
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.
Key words: incremental principal component analysis         feature extraction         developmental robot
收稿日期: 2016-11-17   

1:TP391

基金资助:天津市自然科学基金(15JCYBJC19800,16JCZDJC30400);天津市教委科研计划(20140403)
通讯作者: 葛为民,geweimin@263.net    E-mail: geweimin@263.net
作者简介: 谢自强(1991-),男,硕士生.研究领域:机器人智能学习.
葛为民(1968-),男,博士,教授.研究领域:机器人智能控制.
王肖锋(1977-),男,博士生,讲师.研究领域:可重构机器人动力学控制.

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. 机器人, 2017, 39(2): 189-196.      
XIE Ziqiang, GE Weimin, WANG Xiaofeng, LIU Jun, LIU Zengchang. Real Time Feature Extraction Method of Developmental Robot. ROBOT, 2017, 39(2): 189-196.



链接本文:
http://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0189    或   http://robot.sia.cn/CN/Y2017/V39/I2/189





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