Evolutionary Program Based Approach for Manipulator Grasping Color Objects

Authors: Y. Harold Robinson, M. Rajaram, Honey Raju

Abstract:

Image segmentation and color identification is an important process used in various emerging fields like intelligent robotics. A method is proposed for the manipulator to grasp and place the color object into correct location. The existing methods such as PSO, has problems like accelerating the convergence speed and converging to a local minimum leading to sub optimal performance. To improve the performance, we are using watershed algorithm and for color identification, we are using EPSO. EPSO method is used to reduce the probability of being stuck in the local minimum. The proposed method offers the particles a more powerful global exploration capability. EPSO methods can determine the particles stuck in the local minimum and can also enhance learning speed as the particle movement will be faster.

Keywords: Color information, EPSO, hue, saturation, value (HSV), image segmentation, particle swarm optimization (PSO). Active Contour, GMM.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107197

References:

[1] J. S. Hu and Y. J. Chang, “Calibration of an eye-to-hand system usinga laser pointer on hand and planar constrains,” in Proc. IEEE Int. Conf. Robot. Autom., Shanghai, China, 2011, pp. 982–987.
[2] J. Ning, L. Zhang, D. Zhang, and C.Wu, “Interactive image segmentation by maximal similarity based region merging,” Pattern Recognit.,vol. 43, no. 2, pp. 445–456, Feb. 2010.
[3] H. Li and C. Shen, “Interactive color image segmentation with linear programming,” Mach. Vis. Appl., vol. 21, no. 4, pp. 03–412, Jun.2010.
[4] J. F. Vigueras and M. Rivera, “Registration and interactive planar segmentation for stereo images of polyhedral scenes,” Pattern Recognit.,vol. 43, no. 2, pp. 494–505, Feb. 2010.
[5] S. Xiang, C. Pan, F. Nie, and C. Zhang, “Interactive image segmentation with multiple linear reconstructions in windows,” IEEE Trans.Multimedia, vol. 13, no. 2, pp. 342–352, Apr. 2011.
[6] A. Noma, A. B. V. Graciano, R. M. C. , Jr, L. A. Consularo, and I.Bloch, “Interactive image segmentation by matching attributed relational graphs,” Pattern Recognit., vol. 45, no. 3, pp. 1159–1179, Mar.2012
[7] M. Pardowitz, R. Haschke, J. Steil, and H. Ritter, “Gestalt-based action segmentation for robot task learning,” in Proc. 8th IEEE-RAS Int. Conf. Humanoid Robot., Daejeon, Korea, Dec. 2008, pp. 347–352.
[8] M. Sridharan and P. Stone, “Color learning and illumination invariance on mobile robots: A survey,” Robot. Auton. Syst., vol. 57, no. 6–7, pp. 629–644, Jun. 2009.
[9] W. R. Tan, C. S. Chan, P. Yogarajah, and J. Condell, “A fusion approach for efficient human skin detection,” IEEE Trans. Ind. Informat.,vol. 8, no. 1, pp. 138–147, Feb. 2012.
[10] D. Schiebener, A. Ude, J. Morimotot, T. Asfour, and R. Dillmann, “Segmentation and learning of unknown objects through physical interaction,”in Proc. 11th IEEE-RAS Int. Conf. Humanoid Robot., Bled,Slovenia, Oct. 2011, pp. 500–506.
[11] K. Y. Chan, C. K. F. Yiu, T. S. Dillon, S. Nordholm, and S. H. Ling, “Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization,” IEEE Trans. Ind. Informat.,vol. 8, no. 4, pp. 869–879, Nov. 2012.
[12] F. Tao, D. Zhao, Y. Hu, and Z. Zhou, “Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system,” IEEE Trans. Ind. Informat., vol. 4, no. 4, pp.315–327, Nov. 2008.