Implementation of a neural network based visual motor control algorithm for A 7 DOF redundant manipulator

This paper deals with visual-motor coordination of a 7 dof robot manipulator for pick and place applications. Three issues are dealt with in this paper - finding a feasible inverse kinematic solution without using any orientation information, resolving redundancy at position level and finally maintaining the fidelity of information during clustering process thereby increasing accuracy of inverse kinematic solution. A 3-dimensional KSOM lattice is used to locally linearize the inverse kinematic relationship. The joint angle vector is divided into two groups and their effect on end-effector position is decoupled using a concept called function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. However, this method yields a unique inverse kinematic solution for a given target point. A concept called sub-clustering in configuration space is suggested to preserve redundancy during learning process and redundancy is resolved at position level using several criteria. Even though the training is carried out off-line, the trained network is used online to compute the required joint angle vector in only one step. The accuracy attained is better than the current state of art. The experiment is implemented in real-time and the results are found to corroborate theoretical findings.