A blending crossover differential evolution approach to camera space manipulation parameter optimization

Xie Yu; Zhao Chun-Xia; Zhang Hao-Feng; Yan Xue-Jun; Chen De-Bao

doi:10.7498/aps.64.020701

Abstract

A blending crossover differential evolution algorithm is proposed to increase the precision of camera-space manipulation (CSM) system. In this approach, six view parameters and flattening parameter are assembled into a single parameter of blending crossover differential evolution; the positioning precision of camera-space manipulation is set to be a fitness function.The CSM system can obtain the optimal parameter combination by evolutionary iteration.Experimental results of a virtual robot system show the robot positioning precision is improved by blending crossover differential evolution algorithm.

Keywords:

Authors and contacts

1.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
2.
School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61272220), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61101197) and the Natural Science Foundation of Anhui Province, China (Grant No. 1308085MF82).

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Cited By

[1]	Siciliano B, Villani L 1999 Robot Force Control (Boston: Kluwer)
[2]	Skaar S B, Brockman W H, Hanson R 1987 Int. J. Robot. Res. 6 20
[3]	Skaar S B, Brockman W H, Jang W 1990 Int. J. Robot. Res. 9 22
[4]	Gonzalez-Galvana E J, Loredo-Flores J, Cervantes-Sanchez J, Aguilera-Cortes L A, Steven S B 2008 Robot. Computer-Integrated Manufact. 24 77
[5]	Zhang B, Wang J J, Rossano R, Martinez C, Kock S 2011 IEEE Proceedings of the Robotics and Biomimetics Phuket, Thailand, December 7-11 2011 p944
[6]	Seelinger M, Yode J D, Baumgartner E T, Skaar S B 2002 IEEE Trans. Robot. Automat. 18 957
[7]	Storn R M, Price K V 1997 J. Glob. Optimiz. 11 341
[8]	Price K V, Storn R M, Lampinen J A 2006 Differential Evolution: A Practical Approach to Global Optimization (Berlin: Springer) pp19-54
[9]	Tenaglia G C, Lebensztajn L 2014 IEEE Trans. Magnet. 50 625
[10]	Pan K J, Chen H, Tan Y J 2008 Acta Phys. Sin. 57 5956 (in Chinese) [潘克家, 陈华, 谭永基 2008 物理学报 57 5956]
[11]	Wang J Y, Huang D X 2008 Acta Phys. Sin. 57 2755 (in Chinese) [王钧炎, 黄德先 2008 物理学报 57 2755]
[12]	Zhang W Z, Long W, Jiao J J 2012 Acta Phys. Sin. 61 220506 (in Chinese) [张文专, 龙文, 焦建军 2012 物理学报 61 220506]
[13]	Li N Q, Pan W, Yan L S, Luo B, Xu M F, Jiang N 2011 Chin. Phys. B 20 060502
[14]	Brest J, Greiner S, Boskovic B, Mernik M, Zumer V 2006 IEEE Trans. Evolution. Comput. 10 646
[15]	Liu J, Lampinen J 2005 Soft Comput. 9 448
[16]	Zhang J Q, Sanderson A C 2009 IEEE Trans. Evolution. Comput. 13 945
[17]	Eshelman L J 1993 Foundat. Genetic Algorithms 2 187
[18]	Gonzalez-Galvan E J, Skaar S B, Korde U A, Chen W Z 1997 Int. J. Robot. Res. 16 240
[19]	Turkey M, Poli R 2014 IEEE Trans. Evolution. Comput. 22 159
[20]	He S, Wu Q H, Saunders J R 2009 IEEE Trans. Evolution. Comput. 13 973
[21]	Bjrck A 1996 Numerical Methods for Least Squares Problems 1996 (Philadelphia: Siam)

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Catalog

Vol. 64, Issue 2 - 2015-01-20

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