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

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

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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. 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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 -