Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

Indrani Kar, Laxmidhar Behera

    Research output: Contribution to journalArticle

    11 Citations (Scopus)

    Abstract

    A fuzzy self-organizing map (SOM) network isproposed in this paper for visual motor control of a 7 degreesof freedom (DOF) robot manipulator. The inverse kinematicmap from the image plane to joint angle space of a redundantmanipulator is highly nonlinear and ill-posed in the sensethat a typical end-effector position is associated with severaljoint angle vectors. In the proposed approach, the robotworkspace in image plane is discretized into a number offuzzy regions whose center locations and fuzzy membershipvalues are determined using a Fuzzy C-Mean (FCM)clustering algorithm. SOM network then learns the inversekinematics by on-line by associating a local linear map foreach cluster. A novel learning algorithm has been proposedto make the robot manipulator to reach a target position. Anyarbitrary level of accuracy can be achieved with a numberof fine movements of the manipulator tip. These fine movementsdepend on the error between the target position and thecurrent manipulator position. In particular, the fuzzy modelis found to be better as compared to Kohonen self-organizingmap (KSOM) based learning scheme proposed for visualmotor control. Like existing KSOM learning schemes, theproposed scheme leads to a unique inverse kinematic solutioneven for a redundant manipulator. The proposed algorithmshave been successfully implemented in real-time on a7 DOF PowerCube robot manipulator, and results are foundto concur with the theoretical findings.
    LanguageEnglish
    Pages49-60
    JournalIntelligent Service Robotics
    Volume3
    Issue number1
    DOIs
    Publication statusPublished - 2010

    Fingerprint

    Self organizing maps
    Manipulators
    Robots
    Redundant manipulators
    Inverse kinematics
    End effectors
    Clustering algorithms
    Learning algorithms

    Keywords

    • Redundant manipulator
    • Inverse kinematics
    • Visual motor control
    • Fuzzy self-organizing map network

    Cite this

    Kar, Indrani ; Behera, Laxmidhar. / Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network. In: Intelligent Service Robotics. 2010 ; Vol. 3, No. 1. pp. 49-60.
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    abstract = "A fuzzy self-organizing map (SOM) network isproposed in this paper for visual motor control of a 7 degreesof freedom (DOF) robot manipulator. The inverse kinematicmap from the image plane to joint angle space of a redundantmanipulator is highly nonlinear and ill-posed in the sensethat a typical end-effector position is associated with severaljoint angle vectors. In the proposed approach, the robotworkspace in image plane is discretized into a number offuzzy regions whose center locations and fuzzy membershipvalues are determined using a Fuzzy C-Mean (FCM)clustering algorithm. SOM network then learns the inversekinematics by on-line by associating a local linear map foreach cluster. A novel learning algorithm has been proposedto make the robot manipulator to reach a target position. Anyarbitrary level of accuracy can be achieved with a numberof fine movements of the manipulator tip. These fine movementsdepend on the error between the target position and thecurrent manipulator position. In particular, the fuzzy modelis found to be better as compared to Kohonen self-organizingmap (KSOM) based learning scheme proposed for visualmotor control. Like existing KSOM learning schemes, theproposed scheme leads to a unique inverse kinematic solutioneven for a redundant manipulator. The proposed algorithmshave been successfully implemented in real-time on a7 DOF PowerCube robot manipulator, and results are foundto concur with the theoretical findings.",
    keywords = "Redundant manipulator, Inverse kinematics, Visual motor control, Fuzzy self-organizing map network",
    author = "Indrani Kar and Laxmidhar Behera",
    note = "Reference text: 1. Hutchinson S, Hager GD, Corke PI (1996) A tutorial on visual servo control. IEEE Trans Robot Autom 12(5):651–670 2. Kragic D et al. Survey on visual servoing for manipulation. http:// citeseer.ist.psu.edu/484743.html 3. KupersteinM(1987) Adaptive visual-motor coordination inmultijoint robots using parallel architecture. Proc IEEE Int Conf Robot Autom 4:1595–1602 4. Martinetz TM, Ritter HJ, Schulten KJ (1990) Three-dimensional neural net for learning visual motor coordination of a robot arm. IEEE Trans Neural Netw 1(1):131–136 5. Walter JA, Schulten KJ (1993) Implementation of self-organizing neural networks for visual-motor control of an industrial robot. IEEE Trans Neural Netw 4(1):86–95 6. Behera L, Kirubanandan N (1999) A hybrid neural control scheme for visual-motor coordination. IEEE Control Syst Mag 19(4): 34–41 7. Tsai RY (1987) A versatile camera calibration technique for highaccuracy 3d machine visionmetrology using off-the-shelfTVcameras and lenses. IEEE J Robot Autom RA-3(4):323–344 8. Nakamura Y, Hanafusa H (1984) Task priority based redundancy control of robot manipulators. In: Proceedings of the 2nd internationl symposium on robotic research, Kyoto, Japan 9. Seraji H (1989) Configuration control of redundant manipulators: theory and implementation. IEEE Trans Robot Autom 5(4):472– 490 10. Seraji H, Long MK, Lee TS (1993) Motion control of 7-dof arms: the configuration control approach. IEEE Trans Robot Autom 9(2):125–139 11. Kohonen T (1990) The self-organizing map. In: Proceedings of the IEEE, vol 78, September, pp 1464–1480 12. Kumar N, Behera L (2004) Visual motor coordination using a quantum clustering based neural control scheme. Neural Process Lett 20:11–22 13. Martinetz T, Ritter H, Schulten K (1990) Learning of visuomotorcoordination of a robot arm with redundant degrees of freedom. In: Proceedings of the international conference on parallel processing in neural systems and computers (ICNC). Dusseldorf, Elsevier, Amsterdam, pp 431–434 14. Hesselroth T, Sarkar K, Smagt PP, Schulten K (1994) Neural network control of a pneumatic robot arm. IEEE Trans Syst Man Cybernet 24(1):28–38 15. Kumar S, ShuklaA,Dutta A, Behera L (2007)Amodel-free redundancy resolution technique for visual motor coordination of a 6 dof robot manipulator. In: IEEE 22nd international symposium on intelligent control, 1–3 Oct 2007, pp 544–549 16. Walter J, Ritter H (1996) Rapid learning with parametrized selforganizing maps. Neurocomputing 12:131–153 17. Walter J,NolkerC,RitterH(2000) ThePSOMalgorithm and applications. In: Proceedings of the international ICSC symposium on neural computation, pp 758–764 18. Oh PY, Allen PK (2001) Visual servoing by partitioning degrees of freedom. IEEE Trans Robot Autom 17(1):1–17 19. Roberts RG, Maciejewski AA (1993) Repeatable generalized inverse control strategies for kinematically redundant manipulators. IEEE Trans Autom Control 38(5):689–699 20. English JD, Maciejewski AA (2000) On the implementation of velocity control for kinematically redundant manipulators. IEEE Trans SMC 30(3):233–237 21. Giuseppe RD, Taurisano F, Distante C, Anglani A (1999) Visual servoing of a robotic manipulator based on fuzzy logic controller. In: Proceedings of the international conference on robotics and automation. IEEE, Detroit, Michigan, pp 1487–1494 22. Kim CS, Seo WH, Han SH, Khatib O (2001) Fuzzy logic control of a robot manipulator based on visual servoing. In: Proceedings of the international symposium on industrial electronics. IEEE, Pusan, South Korea, pp 1597–1602 23. Kragic D, Christensen HI (2001) Cue integration in visual servoing. IEEE Trans Robot Autom 17(1):18–27 24. Kumaresan S, Li H, min Li X (1995) Robot hand-eye coordination based on fuzzy logic. In: Fuzzy logic and intelligent systems. Springer, Netherlands, pp 245–269 25. Prochazka A (1996) The fuzzy logic of visuomotor control. Can J Physiol Pharmacol 74(4):456–462 26. Prochazka A, Gillard D (1997) Sensory control of locomotion. In: Proceedings of the American control conference. Albuquerque, New Mexico, pp 2846–2850 27. Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybernet 3:32–57 28. Bezdek JC (1981) Pattern recognition with fuzzy objective function algoritms. Plenum Press, New York 29. D’SouzaA,Vijayakumar S, Schaal S (2001) Learning inverse kinematics. In: International conference on intelligent robots and systems. IEEE, Maui, Hawai, pp 298–303 30. Open source computer vision library, http://www.intel.com/ technology/computing/opencv/ 31. Amtec robotics, http://www.amtec-robotics.com/ 32. Craig JJ (1989) Introduction to robotics. Pearson Education Inc., New Jersey 33. KiviluotoK(1996) Topology preservation in self-organizing maps. In: Proceedings of IEEE international conference on neural networks, vol 1, pp 294–299 34. TevatiaG, Schaal S (2000) Inverse kinematics for humanoid robots. In: IEEE international conference on robotics and automation, vol 1, 24–28 April 2000, pp 294–299 35. Kumar S, Behera L (2008) Visual motor control of a 7dof robot manipulator using function decomposition and sub-clustering in configuration space. Neural Process Lett 28(1):17–33 36. Everest vit, http://www.everestvit.com/",
    year = "2010",
    doi = "10.1007/s11370-009-0058-3",
    language = "English",
    volume = "3",
    pages = "49--60",
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    Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network. / Kar, Indrani; Behera, Laxmidhar.

    In: Intelligent Service Robotics, Vol. 3, No. 1, 2010, p. 49-60.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

    AU - Kar, Indrani

    AU - Behera, Laxmidhar

    N1 - Reference text: 1. Hutchinson S, Hager GD, Corke PI (1996) A tutorial on visual servo control. IEEE Trans Robot Autom 12(5):651–670 2. Kragic D et al. Survey on visual servoing for manipulation. http:// citeseer.ist.psu.edu/484743.html 3. KupersteinM(1987) Adaptive visual-motor coordination inmultijoint robots using parallel architecture. Proc IEEE Int Conf Robot Autom 4:1595–1602 4. Martinetz TM, Ritter HJ, Schulten KJ (1990) Three-dimensional neural net for learning visual motor coordination of a robot arm. IEEE Trans Neural Netw 1(1):131–136 5. Walter JA, Schulten KJ (1993) Implementation of self-organizing neural networks for visual-motor control of an industrial robot. IEEE Trans Neural Netw 4(1):86–95 6. Behera L, Kirubanandan N (1999) A hybrid neural control scheme for visual-motor coordination. IEEE Control Syst Mag 19(4): 34–41 7. Tsai RY (1987) A versatile camera calibration technique for highaccuracy 3d machine visionmetrology using off-the-shelfTVcameras and lenses. IEEE J Robot Autom RA-3(4):323–344 8. Nakamura Y, Hanafusa H (1984) Task priority based redundancy control of robot manipulators. In: Proceedings of the 2nd internationl symposium on robotic research, Kyoto, Japan 9. Seraji H (1989) Configuration control of redundant manipulators: theory and implementation. IEEE Trans Robot Autom 5(4):472– 490 10. Seraji H, Long MK, Lee TS (1993) Motion control of 7-dof arms: the configuration control approach. IEEE Trans Robot Autom 9(2):125–139 11. Kohonen T (1990) The self-organizing map. In: Proceedings of the IEEE, vol 78, September, pp 1464–1480 12. Kumar N, Behera L (2004) Visual motor coordination using a quantum clustering based neural control scheme. Neural Process Lett 20:11–22 13. Martinetz T, Ritter H, Schulten K (1990) Learning of visuomotorcoordination of a robot arm with redundant degrees of freedom. In: Proceedings of the international conference on parallel processing in neural systems and computers (ICNC). Dusseldorf, Elsevier, Amsterdam, pp 431–434 14. Hesselroth T, Sarkar K, Smagt PP, Schulten K (1994) Neural network control of a pneumatic robot arm. IEEE Trans Syst Man Cybernet 24(1):28–38 15. Kumar S, ShuklaA,Dutta A, Behera L (2007)Amodel-free redundancy resolution technique for visual motor coordination of a 6 dof robot manipulator. In: IEEE 22nd international symposium on intelligent control, 1–3 Oct 2007, pp 544–549 16. Walter J, Ritter H (1996) Rapid learning with parametrized selforganizing maps. Neurocomputing 12:131–153 17. Walter J,NolkerC,RitterH(2000) ThePSOMalgorithm and applications. In: Proceedings of the international ICSC symposium on neural computation, pp 758–764 18. Oh PY, Allen PK (2001) Visual servoing by partitioning degrees of freedom. IEEE Trans Robot Autom 17(1):1–17 19. Roberts RG, Maciejewski AA (1993) Repeatable generalized inverse control strategies for kinematically redundant manipulators. IEEE Trans Autom Control 38(5):689–699 20. English JD, Maciejewski AA (2000) On the implementation of velocity control for kinematically redundant manipulators. IEEE Trans SMC 30(3):233–237 21. Giuseppe RD, Taurisano F, Distante C, Anglani A (1999) Visual servoing of a robotic manipulator based on fuzzy logic controller. In: Proceedings of the international conference on robotics and automation. IEEE, Detroit, Michigan, pp 1487–1494 22. Kim CS, Seo WH, Han SH, Khatib O (2001) Fuzzy logic control of a robot manipulator based on visual servoing. In: Proceedings of the international symposium on industrial electronics. IEEE, Pusan, South Korea, pp 1597–1602 23. Kragic D, Christensen HI (2001) Cue integration in visual servoing. IEEE Trans Robot Autom 17(1):18–27 24. Kumaresan S, Li H, min Li X (1995) Robot hand-eye coordination based on fuzzy logic. In: Fuzzy logic and intelligent systems. Springer, Netherlands, pp 245–269 25. Prochazka A (1996) The fuzzy logic of visuomotor control. Can J Physiol Pharmacol 74(4):456–462 26. Prochazka A, Gillard D (1997) Sensory control of locomotion. In: Proceedings of the American control conference. Albuquerque, New Mexico, pp 2846–2850 27. Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybernet 3:32–57 28. Bezdek JC (1981) Pattern recognition with fuzzy objective function algoritms. Plenum Press, New York 29. D’SouzaA,Vijayakumar S, Schaal S (2001) Learning inverse kinematics. In: International conference on intelligent robots and systems. IEEE, Maui, Hawai, pp 298–303 30. Open source computer vision library, http://www.intel.com/ technology/computing/opencv/ 31. Amtec robotics, http://www.amtec-robotics.com/ 32. Craig JJ (1989) Introduction to robotics. Pearson Education Inc., New Jersey 33. KiviluotoK(1996) Topology preservation in self-organizing maps. In: Proceedings of IEEE international conference on neural networks, vol 1, pp 294–299 34. TevatiaG, Schaal S (2000) Inverse kinematics for humanoid robots. In: IEEE international conference on robotics and automation, vol 1, 24–28 April 2000, pp 294–299 35. Kumar S, Behera L (2008) Visual motor control of a 7dof robot manipulator using function decomposition and sub-clustering in configuration space. Neural Process Lett 28(1):17–33 36. Everest vit, http://www.everestvit.com/

    PY - 2010

    Y1 - 2010

    N2 - A fuzzy self-organizing map (SOM) network isproposed in this paper for visual motor control of a 7 degreesof freedom (DOF) robot manipulator. The inverse kinematicmap from the image plane to joint angle space of a redundantmanipulator is highly nonlinear and ill-posed in the sensethat a typical end-effector position is associated with severaljoint angle vectors. In the proposed approach, the robotworkspace in image plane is discretized into a number offuzzy regions whose center locations and fuzzy membershipvalues are determined using a Fuzzy C-Mean (FCM)clustering algorithm. SOM network then learns the inversekinematics by on-line by associating a local linear map foreach cluster. A novel learning algorithm has been proposedto make the robot manipulator to reach a target position. Anyarbitrary level of accuracy can be achieved with a numberof fine movements of the manipulator tip. These fine movementsdepend on the error between the target position and thecurrent manipulator position. In particular, the fuzzy modelis found to be better as compared to Kohonen self-organizingmap (KSOM) based learning scheme proposed for visualmotor control. Like existing KSOM learning schemes, theproposed scheme leads to a unique inverse kinematic solutioneven for a redundant manipulator. The proposed algorithmshave been successfully implemented in real-time on a7 DOF PowerCube robot manipulator, and results are foundto concur with the theoretical findings.

    AB - A fuzzy self-organizing map (SOM) network isproposed in this paper for visual motor control of a 7 degreesof freedom (DOF) robot manipulator. The inverse kinematicmap from the image plane to joint angle space of a redundantmanipulator is highly nonlinear and ill-posed in the sensethat a typical end-effector position is associated with severaljoint angle vectors. In the proposed approach, the robotworkspace in image plane is discretized into a number offuzzy regions whose center locations and fuzzy membershipvalues are determined using a Fuzzy C-Mean (FCM)clustering algorithm. SOM network then learns the inversekinematics by on-line by associating a local linear map foreach cluster. A novel learning algorithm has been proposedto make the robot manipulator to reach a target position. Anyarbitrary level of accuracy can be achieved with a numberof fine movements of the manipulator tip. These fine movementsdepend on the error between the target position and thecurrent manipulator position. In particular, the fuzzy modelis found to be better as compared to Kohonen self-organizingmap (KSOM) based learning scheme proposed for visualmotor control. Like existing KSOM learning schemes, theproposed scheme leads to a unique inverse kinematic solutioneven for a redundant manipulator. The proposed algorithmshave been successfully implemented in real-time on a7 DOF PowerCube robot manipulator, and results are foundto concur with the theoretical findings.

    KW - Redundant manipulator

    KW - Inverse kinematics

    KW - Visual motor control

    KW - Fuzzy self-organizing map network

    U2 - 10.1007/s11370-009-0058-3

    DO - 10.1007/s11370-009-0058-3

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    EP - 60

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    JF - Intelligent Service Robotics

    SN - 1861-2776

    IS - 1

    ER -