Visual motor control of a 6 DOF robot manipulator using a fuzzy learning paradigm

This paper is concerned with the inverse kinematic control of a 6 DOF robot manipulator using visual feedback. Two different frameworks have been proposed to learn the inverse kinematics of the manipulator. In the first framework, the robot work-space has been discretized using a priori fixed number of fuzzy regions. Within each fuzzy region, the inverse kinematic relationship from image plane as observed by two fixed cameras to joint space of the manipulator is expressed as a linear map using first order approximation. This proposed framework allows the inverse kinematics to be represented by a Takagi-Sugeno (T-S) fuzzy model whose parameters are learned on-line using gradient descent algorithm. In the second framework, the robot workspace in image plane is discretized into a number of clusters whose centers are determined using Fuzzy C Mean (FCM) clustering algorithm. The FCM algorithm allows each data vector to belong to every cluster with a fuzzy truth value between 0 and 1. The inverse kinematics problem is solved without using any knowledge about orientation of the manipulator. This leads to redundant solutions in the joint angle space for a given target position. This redundancy in the joint angle space is achieved using the concept of sub clustering in the joint space. Inclusion of sub-clustering also improves the position tracking accuracy. The proposed algorithms have been successfully implemented on a 6 DOF PowerCube manipulator from Amtec robotics with a reasonable position tracking accuracy.