Visual Motor Control of a 7 DOF Robot Manipulator Using Function Decomposition and Sub-Clustering in Configuration Space |
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Authors: | Swagat Kumar Naman Patel Laxmidhar Behera |
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Affiliation: | (1) Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208 016, Uttar Pradesh, India;(2) Present address: School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Ulster, UK |
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Abstract: | This paper deals with real-time implementation of visual-motor control of a 7 degree of freedom (DOF) robot manipulator using
self-organized map (SOM) based learning approach. The robot manipulator considered here is a 7 DOF PowerCube manipulator from
Amtec Robotics. The primary objective is to reach a target point in the task space using only a single step movement from
any arbitrary initial configuration of the robot manipulator. A new clustering algorithm using Kohonen SOM lattice has been
proposed that maintains the fidelity of training data. Two different approaches have been proposed to find an inverse kinematic
solution without using any orientation feedback. In the first approach, the inverse Jacobian matrices are learnt from the
training data using function decomposition. It is shown that function decomposition leads to significant improvement in accuracy
of inverse kinematic solution. In the second approach, a concept called sub-clustering in configuration space is suggested
to provide multiple solutions for the inverse kinematic problem. Redundancy is resolved at position level using several criteria.
A redundant manipulator is dexterous owing to the availability of multiple configurations for a given end-effector position.
However, existing visual motor coordination schemes provide only one inverse kinematic solution for every target position
even when the manipulator is kinematically redundant. Thus, the second approach provides a learning architecture that can
capture redundancy from the training data. The training data are generated using explicit kinematic model of the combined
robot manipulator and camera configuration. The training is carried out off-line and the trained network is used on-line to
compute the joint angle vector to reach a target position in a single step only. The accuracy attained is better than the
current state of art. |
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Keywords: | VMC Visual motor coordination 7 DOF robot manipulators PowerCube Function decomposition Sub-clustering Redundancy resolution KSOM Inverse kinematics |
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