Inverse Kinematic Control Using Rotational And Joint Space Clustering With Visual Motor Coordination

Abstract

In this paper, the inverse kinematic control of a 6-DOF robot manipulator is achieved using visual motor coordination (VMC). Here the positional data is converted into image plane data of a pair of cameras. The Redundancy resolution is a prime goal for the robot manipulator with higher dimensional joint space than the task-space. In this work, we present five schemes for this redundancy resolution based on hybrid visual motor co-ordination (VMC) for a 6-dof robot manipulator by clustering the rotational space and joint space information with visual feedback from a pair of cameras. The proposed schemes are used with the extended Kohonen's Self Organizing Map (EKSOM) to find out the mapping from 3-dimensional positional task space to the 6-dimensional joint space of the manipulator. The neural network with EKSOM is modified to use the cyclic nature of angular displacement of joints. The visual feedback is obtained through a pair of calibrated cameras. So, each positional data is converted to corresponding camera coordinates and then the modified EKSOM has been trained to obtain the input-output mapping by combining the visual feedback and hybrid system model consisting of forward kinematics of the manipulator. These methods produce smooth joint movements for positional tracking. These schemes are successfully implemented on a model of 6-DOF PowerCube™ robot manipulator from Amtec Robotics.