Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-25T05:38:54.711Z Has data issue: false hasContentIssue false
  • English
  • Français

Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network

Published online by Cambridge University Press:  21 September 2009

Swagat Kumar ,
Premkumar P. ,
Ashish Dutta  and
Laxmidhar Behera
Swagat Kumar
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Premkumar P.
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Ashish Dutta
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Laxmidhar Behera*
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India School of Computing and Intelligent systems, University of Ulster, Magee, Northern Ireland, UK
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.

Keywords

Visual motor controlSelf-organizing mapSub-clusteringRedundancy resolutionInverse kinematics
Type
Article
Information
Robotica , Volume 28 , Issue 6 , October 2010 , pp. 795 - 810
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Angulo, V. R. and Torras, C., “Speeding up the learning of robot kinematics through function decomposition,” IEEE Trans. Neural Networks 16 (6), 1504–1512 (Nov. 2005).Google Scholar
2.Barreto, G. A., Araujo, A. F. R. and Ritter, H. J., “Self-organizing feature maps for modeling and control of robotic manipulators,” J. Intell. Rob. Syst. 36, 407–450 (2003).CrossRefGoogle Scholar
3.Behera, L. and Kirubanandan, N., “A hybrid neural control scheme for visual-motor coordination,” IEEE Control Syst. Mag. 19 (4), 34–41 (1999).Google Scholar
4.Chaumette, F., “Image moments: A general and useful set of features for visual servoing,” IEEE Trans. Rob. 20 (4), 713–723 (Aug. 2004).CrossRefGoogle Scholar
5.Chaumette, F. and Marchand, E., “A redundancy-based iterative approach for avoiding joint limits: Application to visual servoing,” IEEE Trans. Rob. Automat. 17 (5), 719–730 (Oct. 2001).CrossRefGoogle Scholar
6.Feddema, J. T., George Lee, C. S. and Mitchell, O. W., “Weighted selection of image features for resolved rate visual feedback control,” IEEE Trans. Rob. Automat. 7 (1), 31–47 (Feb. 1991).CrossRefGoogle Scholar
7.Han, M., Okada, N. and Kondo, E., “Coordination of an uncalibrated 3-d visuo-motor system based on multiple self-organizing maps,” JSME Int. J. Ser. C 49 (1), 230–239 (2006).CrossRefGoogle Scholar
8.Hutchinson, S., Hager, G. D. and Corke, P. I., “A tutorial on visual servo control,” IEEE Trans. Rob. Automat. 12 (5), 651–670 (Oct. 1996).CrossRefGoogle Scholar
9.Jiang, P., Bamforth, L. C. A., Feng, Z., Baruch, J. E. F. and Chen, Y. Q., “Indirect iterative learning control for a discrete visual servo without a camera-robot model,” IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 37 (4), 863–876 (Aug. 2007).CrossRefGoogle ScholarPubMed
10.Kohonen, T., Self Organization and Associative Memory (Springer-Verlag, Berlin, Germany, 1984).Google Scholar
11.Kragic, D. and Christensen, H. I., Survey on Visual Servoing for Manipulation Technical Report (Stockholm, Sweden: Computational Vision and Active Perception Laboratory, KTH, 2002).Google Scholar
12.Kumar, N. and Behera, L., “Visual motor coordination using a quantum clustering based neural control scheme,” Neural Process. Lett. 20, 11–22 (2004).CrossRefGoogle Scholar
13.Kumar, S. and Behera, L., “Implementation of a Neural Network Based Visual Motor Control Algorithm for a 7 dof Redundant Manipulator,” International Joint Conference on Neural Networks (IJCNN), Hong Kong, China (June 2008) pp. 1344–1351.Google Scholar
14.Kumar, S., Patel, N. and Behera, L., “Visual motor control of a 7 dof robot manipulator using function decomposition and sub-clustering in configuration space,” Neural Process. Lett. 28 (1), 17–33 (Aug. 2008).CrossRefGoogle Scholar
15.Li, L., Gruver, W. A., Zhang, Q. and Yang, Z., “Kinematic control of redundant robots and the motion optimizability measure,” IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 31 (1), 155–160 (Feb. 2001).CrossRefGoogle ScholarPubMed
16.Li, Y. and Leong, S. H., “Kinematics control of redundant manipulators using a CMAC neural network combined with a genetic algorithm,” Robotica 22, 611–621 (2004).CrossRefGoogle Scholar
17.Martinetz, T., Ritter, H. and Schulten, K., “Learning of visuomotor-coordination of a robot arm with redundant degrees of freedom,” In Proceedings of the International Conference on Parallel Processing in Neural Systems and Computers (ICNC), (Elsevier, Dusseldorf and Amsterdam 1990) pp. 431–434.Google Scholar
18.Martinetz, T. M., Ritter, H. J. and Schulten, K. J., “Three-dimensional neural net for learning visual motor coordination of a robot arm,” IEEE Trans. Neural Networks 1 (1), 131–136 (Mar. 1990).CrossRefGoogle Scholar
19.Mayorgaa, R. I. V. and Sanongboone, P., “Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach,” Rob. Auton. Syst. 53, 164–176 (2005).CrossRefGoogle Scholar
20.Sharma, R. and Hutchinson, S., “Optimizing Hand/Eye Configuration for Visual-Servo Systems,” Proceedings of the International Conference on Robotics and Automation (ICRA), Nagoya, Japan (May 1995) pp. 172–177.Google Scholar
21.Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control, New York, USA (John Wiley, 1989).Google Scholar
22.Tevatia, G. and Schaal, S., “Inverse Kinematics of Humanoid Robots.” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA (Apr. 2000) pp. 294–299.Google Scholar
23.Tsai, R. Y., “A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE J. Rob. Automat. RA-3 (4), 323–344 (Aug. 1987).CrossRefGoogle Scholar
24.Walter, J. A. and Schulten, K. J., “Implementation of self-organizing neural networks for visual-motor control of an industrial robot,” IEEE Trans. Neural Networks 4 (1), 86–95 (Jan. 1993).CrossRefGoogle Scholar
25.Wilson, R., “Tsai Camera Calibration Software,” available at http://www.cs.cmu.edu/~rgw/TsaiCode.html.Google Scholar
26.Xia, Y. and Wang, J., “A dual neural network for kinematic control of redundant robot manipulators,” IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 31 (1), 147–154 (Feb. 2001).Google ScholarPubMed
27.Zha, H., Onitsuka, T. and Nagata, T., “A self-organization learning algorithm for visuo-motor coordination in unstructured environment,” Artif. Life Rob. 1 (3), 131–136 (Sep. 1997).CrossRefGoogle Scholar
28.Zheng, X.-Z. and Ito, K., “Self-organized learning and its implementation of robot movements,” IEEE International Conference on SMC, “Computational Cybernetics and Simulation,” Orlando, FL (1997) pp. 281–286.Google Scholar