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

Swagat Kumar, Premkumar Patcaikani, Ashish Dutta, Laxmidhar Behera

    Research output: Contribution to journalArticle

    21 Citations (Scopus)

    Abstract

    This paper deals with the design and implementation of avisual kinematic control scheme for a redundant manipulator.The inverse kinematic map for a redundant manipulatoris a one-to-many relation problem; i.e. for each Cartesianposition, multiple joint angle vectors are associated. Whenthis inverse kinematic relation is learnt using existinglearning schemes, a single inverse kinematic solution isachieved, although the manipulator is redundant. Thus anew redundancy preserving network based on the selforganizingmap (SOM) has been proposed to learn theone-to-many relation using sub-clustering in joint anglespace. The SOM network resolves redundancy using threecriteria, namely lazy arm movement, minimum angle normand minimum condition number of image Jacobian matrix.The proposed scheme is able to guide the manipulator endeffectortowards the desired target within 1-mm positioningaccuracy without exceeding physical joint angle limits. Anew concept of neighbourhood has been introduced toenable the manipulator to follow any continuous trajectory.The proposed scheme has been implemented on a sevendegree-of-freedom (7DOF) PowerCube robot manipulatorsuccessfully with visual position feedback only. Thepositioning accuracy of the redundant manipulator usingthe proposed scheme outperforms existing SOM-basedalgorithms
    LanguageEnglish
    Pages795-810
    JournalRobotica
    Volume28
    DOIs
    Publication statusPublished - 2009

    Fingerprint

    Redundant Manipulator
    Redundant manipulators
    Motor Control
    Inverse kinematics
    Redundancy
    Inverse Kinematics
    Manipulator
    Manipulators
    Jacobian matrices
    Angle
    Kinematics
    One to many
    Trajectories
    Robots
    Feedback
    Jacobian matrix
    Condition number
    Resolve
    Robot
    Vision

    Keywords

    • Visual motor control
    • Self-organizing map
    • Sub-clustering
    • Redundancy resolution
    • Inverse kinematics.

    Cite this

    Kumar, Swagat ; Patcaikani, Premkumar ; Dutta, Ashish ; Behera, Laxmidhar. / Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network. In: Robotica. 2009 ; Vol. 28. pp. 795-810.
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    abstract = "This paper deals with the design and implementation of avisual kinematic control scheme for a redundant manipulator.The inverse kinematic map for a redundant manipulatoris a one-to-many relation problem; i.e. for each Cartesianposition, multiple joint angle vectors are associated. Whenthis inverse kinematic relation is learnt using existinglearning schemes, a single inverse kinematic solution isachieved, although the manipulator is redundant. Thus anew redundancy preserving network based on the selforganizingmap (SOM) has been proposed to learn theone-to-many relation using sub-clustering in joint anglespace. The SOM network resolves redundancy using threecriteria, namely lazy arm movement, minimum angle normand minimum condition number of image Jacobian matrix.The proposed scheme is able to guide the manipulator endeffectortowards the desired target within 1-mm positioningaccuracy without exceeding physical joint angle limits. Anew concept of neighbourhood has been introduced toenable the manipulator to follow any continuous trajectory.The proposed scheme has been implemented on a sevendegree-of-freedom (7DOF) PowerCube robot manipulatorsuccessfully with visual position feedback only. Thepositioning accuracy of the redundant manipulator usingthe proposed scheme outperforms existing SOM-basedalgorithms",
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    author = "Swagat Kumar and Premkumar Patcaikani and Ashish Dutta and Laxmidhar Behera",
    note = "Reference text: 1. V. R. Angulo and C. Torras, “Speeding up the learning of robot kinematics through function decomposition,” IEEE Trans. Neural Networks 16(6), 1504–1512 (Nov. 2005). 2. G. A. Barreto, A. F. R. Araujo and H. J. Ritter, “Selforganizing feature maps for modeling and control of robotic manipulators,” J. Intell. Rob. Syst. 36, 407–450 (2003). 3. L. Behera and N. Kirubanandan, “A hybrid neural control scheme for visual-motor coordination,” IEEE Control Syst. Mag. 19(4), 34–41 (1999). 4. F. Chaumette, “Image moments: A general and useful set of features for visual servoing,” IEEE Trans. Rob. 20(4), 713–723 (Aug. 2004). 5. F. Chaumette and E. Marchand, “A redundancy-based iterative approach for avoiding joint limits: Application to visual servoing,” IEEE Trans. Rob. Automat. 17(5), 719–730 (Oct. 2001). 6. J. T. Feddema, C. S. George Lee and O. W. Mitchell, “Weighted selection of image features for resolved rate visual feedback control,” IEEE Trans. Rob. Automat. 7(1), 31–47 (Feb. 1991). 7. M. Han, N. Okada and E. Kondo, “Coordination of an uncalibrated 3-d visuo-motor system based on multiple selforganizing maps,” JSME Int. J. Ser. C 49(1), 230–239 (2006). 8. S. Hutchinson, G. D. Hager and P. I. Corke, “A tutorial on visual servo control,” IEEE Trans. Rob. Automat. 12(5), 651– 670 (Oct. 1996). 9. P. Jiang, L. C. A. Bamforth, Z. Feng, J. E. F. Baruch and Y. Q. Chen, “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). 10. T. Kohonen, Self Organization and Associative Memory (Springer-Verlag, Berlin, Germany, 1984). 11. D. Kragic and H. I. Christensen, Survey on Visual Servoing for Manipulation Technical Report (Stockholm, Sweden: ComputationalVision and Active Perception Laboratory, KTH, 2002). 12. N. Kumar and L. Behera, “Visual motor coordination using a quantum clustering based neural control scheme,” Neural Process. Lett. 20, 11–22 (2004). 13. S. Kumar and L. Behera, “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. 14. S. Kumar, N. Patel and L. Behera, “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). 15. L. Li,W. A. Gruver, Q. Zhang and Z. Yang, “Kinematic control of redundant robots and the motion optimizability measure,” IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 31(1), 155–160 (Feb. 2001). 16. Y. Li and S. H. Leong, “Kinematics control of redundant manipulators using a CMAC neural network combined with a genetic algorithm,” Robotica 22, 611–621 (2004). 17. T. Martinetz, H. Ritter and K. Schulten, “Learning of visuomotor-coordination of a robot armwith 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. 18. T. M. Martinetz, H. J. Ritter and K. J. Schulten, “Threedimensional neural net for learning visual motor coordination of a robot arm,” IEEE Trans. Neural Networks 1(1), 131–136 (Mar. 1990). 19. R. I.V. Mayorgaa and P. Sanongboone, “Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach,” Rob. Auton. Syst. 53, 164–176 (2005). 20. R. Sharma and S. Hutchinson, “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. 21. M.W. Spong andM.Vidyasagar, Robot Dynamics and Control, New York, USA (John Wiley, 1989). 22. G. Tevatia and S. Schaal, “Inverse Kinematics of Humanoid Robots.” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA (Apr. 2000) pp. 294–299. 23. R. Y. Tsai, “A versatile camera calibration technique for highaccuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE J. Rob. Automat. RA-3(4), 323–344 (Aug. 1987). 24. J. A. Walter and K. J. Schulten, “Implementation of selforganizing neural networks for visual-motor control of an industrial robot,” IEEE Trans. Neural Networks 4(1), 86–95 (Jan. 1993). 25. R. Wilson, “Tsai Camera Calibration Software,” available at http://www.cs.cmu.edu/ rgw/TsaiCode.html. 26. Y. Xia and J. Wang, “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). 27. H. Zha, T. Onitsuka and T. Nagata, “A self-organization learning algorithm for visuo-motor coordination in unstructured environment,” Artif. Life Rob. 1(3), 131–136 (Sep. 1997). 28. X.-Z. Zheng and K. Ito, “Self-organized learning and its implementation of robot movements,” IEEE International Conference on SMC, “Computational Cybernetics and Simulation,” Orlando, FL (1997) pp. 281–286.",
    year = "2009",
    doi = "10.1017/S026357470999049X",
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    pages = "795--810",
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    Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network. / Kumar, Swagat; Patcaikani, Premkumar; Dutta, Ashish; Behera, Laxmidhar.

    In: Robotica, Vol. 28, 2009, p. 795-810.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network

    AU - Kumar, Swagat

    AU - Patcaikani, Premkumar

    AU - Dutta, Ashish

    AU - Behera, Laxmidhar

    N1 - Reference text: 1. V. R. Angulo and C. Torras, “Speeding up the learning of robot kinematics through function decomposition,” IEEE Trans. Neural Networks 16(6), 1504–1512 (Nov. 2005). 2. G. A. Barreto, A. F. R. Araujo and H. J. Ritter, “Selforganizing feature maps for modeling and control of robotic manipulators,” J. Intell. Rob. Syst. 36, 407–450 (2003). 3. L. Behera and N. Kirubanandan, “A hybrid neural control scheme for visual-motor coordination,” IEEE Control Syst. Mag. 19(4), 34–41 (1999). 4. F. Chaumette, “Image moments: A general and useful set of features for visual servoing,” IEEE Trans. Rob. 20(4), 713–723 (Aug. 2004). 5. F. Chaumette and E. Marchand, “A redundancy-based iterative approach for avoiding joint limits: Application to visual servoing,” IEEE Trans. Rob. Automat. 17(5), 719–730 (Oct. 2001). 6. J. T. Feddema, C. S. George Lee and O. W. Mitchell, “Weighted selection of image features for resolved rate visual feedback control,” IEEE Trans. Rob. Automat. 7(1), 31–47 (Feb. 1991). 7. M. Han, N. Okada and E. Kondo, “Coordination of an uncalibrated 3-d visuo-motor system based on multiple selforganizing maps,” JSME Int. J. Ser. C 49(1), 230–239 (2006). 8. S. Hutchinson, G. D. Hager and P. I. Corke, “A tutorial on visual servo control,” IEEE Trans. Rob. Automat. 12(5), 651– 670 (Oct. 1996). 9. P. Jiang, L. C. A. Bamforth, Z. Feng, J. E. F. Baruch and Y. Q. Chen, “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). 10. T. Kohonen, Self Organization and Associative Memory (Springer-Verlag, Berlin, Germany, 1984). 11. D. Kragic and H. I. Christensen, Survey on Visual Servoing for Manipulation Technical Report (Stockholm, Sweden: ComputationalVision and Active Perception Laboratory, KTH, 2002). 12. N. Kumar and L. Behera, “Visual motor coordination using a quantum clustering based neural control scheme,” Neural Process. Lett. 20, 11–22 (2004). 13. S. Kumar and L. Behera, “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. 14. S. Kumar, N. Patel and L. Behera, “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). 15. L. Li,W. A. Gruver, Q. Zhang and Z. Yang, “Kinematic control of redundant robots and the motion optimizability measure,” IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 31(1), 155–160 (Feb. 2001). 16. Y. Li and S. H. Leong, “Kinematics control of redundant manipulators using a CMAC neural network combined with a genetic algorithm,” Robotica 22, 611–621 (2004). 17. T. Martinetz, H. Ritter and K. Schulten, “Learning of visuomotor-coordination of a robot armwith 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. 18. T. M. Martinetz, H. J. Ritter and K. J. Schulten, “Threedimensional neural net for learning visual motor coordination of a robot arm,” IEEE Trans. Neural Networks 1(1), 131–136 (Mar. 1990). 19. R. I.V. Mayorgaa and P. Sanongboone, “Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach,” Rob. Auton. Syst. 53, 164–176 (2005). 20. R. Sharma and S. Hutchinson, “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. 21. M.W. Spong andM.Vidyasagar, Robot Dynamics and Control, New York, USA (John Wiley, 1989). 22. G. Tevatia and S. Schaal, “Inverse Kinematics of Humanoid Robots.” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA (Apr. 2000) pp. 294–299. 23. R. Y. Tsai, “A versatile camera calibration technique for highaccuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE J. Rob. Automat. RA-3(4), 323–344 (Aug. 1987). 24. J. A. Walter and K. J. Schulten, “Implementation of selforganizing neural networks for visual-motor control of an industrial robot,” IEEE Trans. Neural Networks 4(1), 86–95 (Jan. 1993). 25. R. Wilson, “Tsai Camera Calibration Software,” available at http://www.cs.cmu.edu/ rgw/TsaiCode.html. 26. Y. Xia and J. Wang, “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). 27. H. Zha, T. Onitsuka and T. Nagata, “A self-organization learning algorithm for visuo-motor coordination in unstructured environment,” Artif. Life Rob. 1(3), 131–136 (Sep. 1997). 28. X.-Z. Zheng and K. Ito, “Self-organized learning and its implementation of robot movements,” IEEE International Conference on SMC, “Computational Cybernetics and Simulation,” Orlando, FL (1997) pp. 281–286.

    PY - 2009

    Y1 - 2009

    N2 - This paper deals with the design and implementation of avisual kinematic control scheme for a redundant manipulator.The inverse kinematic map for a redundant manipulatoris a one-to-many relation problem; i.e. for each Cartesianposition, multiple joint angle vectors are associated. Whenthis inverse kinematic relation is learnt using existinglearning schemes, a single inverse kinematic solution isachieved, although the manipulator is redundant. Thus anew redundancy preserving network based on the selforganizingmap (SOM) has been proposed to learn theone-to-many relation using sub-clustering in joint anglespace. The SOM network resolves redundancy using threecriteria, namely lazy arm movement, minimum angle normand minimum condition number of image Jacobian matrix.The proposed scheme is able to guide the manipulator endeffectortowards the desired target within 1-mm positioningaccuracy without exceeding physical joint angle limits. Anew concept of neighbourhood has been introduced toenable the manipulator to follow any continuous trajectory.The proposed scheme has been implemented on a sevendegree-of-freedom (7DOF) PowerCube robot manipulatorsuccessfully with visual position feedback only. Thepositioning accuracy of the redundant manipulator usingthe proposed scheme outperforms existing SOM-basedalgorithms

    AB - This paper deals with the design and implementation of avisual kinematic control scheme for a redundant manipulator.The inverse kinematic map for a redundant manipulatoris a one-to-many relation problem; i.e. for each Cartesianposition, multiple joint angle vectors are associated. Whenthis inverse kinematic relation is learnt using existinglearning schemes, a single inverse kinematic solution isachieved, although the manipulator is redundant. Thus anew redundancy preserving network based on the selforganizingmap (SOM) has been proposed to learn theone-to-many relation using sub-clustering in joint anglespace. The SOM network resolves redundancy using threecriteria, namely lazy arm movement, minimum angle normand minimum condition number of image Jacobian matrix.The proposed scheme is able to guide the manipulator endeffectortowards the desired target within 1-mm positioningaccuracy without exceeding physical joint angle limits. Anew concept of neighbourhood has been introduced toenable the manipulator to follow any continuous trajectory.The proposed scheme has been implemented on a sevendegree-of-freedom (7DOF) PowerCube robot manipulatorsuccessfully with visual position feedback only. Thepositioning accuracy of the redundant manipulator usingthe proposed scheme outperforms existing SOM-basedalgorithms

    KW - Visual motor control

    KW - Self-organizing map

    KW - Sub-clustering

    KW - Redundancy resolution

    KW - Inverse kinematics.

    U2 - 10.1017/S026357470999049X

    DO - 10.1017/S026357470999049X

    M3 - Article

    VL - 28

    SP - 795

    EP - 810

    JO - Robotica

    T2 - Robotica

    JF - Robotica

    SN - 0263-5747

    ER -