Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis

WANG Xiaofeng; ZHANG Minglu; LIU Jun

doi:10.11999/JEIT170561

Volume 40 Issue 3

Mar. 2018

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WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561

Citation:

WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561

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Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis

doi: 10.11999/JEIT170561

1.
2.
(School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

Funds:

The National Natural Science Foundation of China (61503119, 61473113), The Tianjin Natural Science Foundation (15JCYBJC19800, 16JCZDJC30400), The Tianjin Intelligent Manufacturing and Technology Key Project (15ZXZNGX00090)

Received Date: 2017-06-09
Rev Recd Date: 2017-10-13
Publish Date: 2018-03-19

Abstract

Abstract

Existing Candid Covariance-free Incremental PCA (CCIPCA) has the limitation of the stable image inherent covariance, and a Generalized CCIPCA (GCCIPCA) with an appended term of the mean difference vector is presented. It can be considered that the CCIPCA is only a special case of the GCCIPCA and can extend the scope of the algorithm. Then, the incremental learning of the proposed GCCIPCA is innovated to the existing Bi-Directional PCA (BDPCA), and the called Incremental BDPCA (IBDPCA) is used for the robot perceptual learning and it can be used to incrementally compute the principal components without estimating the similar scatter matrixes in the row and column directions, which can build up the real-time processing speed greatly. Finally, the blocks grasped by the robot are used as the perceptual objects, and the experimental results demonstrate that the proposed algorithm works well, and the convergence rate, the classification recognition rate, the computation time and the required memory are improved significantly.
- Robot perceptual learning,
- Incremental learning,
- Dimension reduction,
- Candid Covariance-free Incremental PCA (CCIPCA),
- Bi-Directional PCA (BDPCA)

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References(25)

References

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