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

Indrani Kar; Laxmidhar Behera

doi:10.1007/s11370-009-0058-3

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

Indrani Kar, Laxmidhar Behera

Research output: Contribution to journal › Article › peer-review

14 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.

Original language	English
Pages (from-to)	49-60
Journal	Intelligent Service Robotics
Volume	3
Issue number	1
DOIs	https://doi.org/10.1007/s11370-009-0058-3
Publication status	Published (in print/issue) - 2010

Bibliographical 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/

Keywords

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

Access to Document

10.1007/s11370-009-0058-3

http://www.springerlink.com/content/t467v476q84613x8/

Cite this

@article{3637e33ab0534ee3b18a0ad46f3ef294,

title = "Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network",

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{\textquoteright}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",

journal = "Intelligent Service Robotics",

issn = "1861-2784",

publisher = "Springer",

number = "1",

}

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

M3 - Article

SN - 1861-2784

VL - 3

SP - 49

EP - 60

JO - Intelligent Service Robotics

JF - Intelligent Service Robotics

IS - 1

ER -

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

Abstract

Bibliographical note

Keywords

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Cite this