Innovative Production Machines and Systems Conference

Position and orientation accuracy of the end effector is affected by
the precision of kinematic parameters of the robot manipulator. Thus
good precision requires good knowledge of robot physical parameter
values. However this condition can be difficult to meet in practice.
Hence calibration techniques can be devised in order to improve the
robot accuracy through estimation of these particular parameters. In
this paper, the Genetic Algorithm is used to calibrate the robot
kinematic accuracy. A kinematic model is formulated and conducted as an
optimization problem for ABB robot manipulators. The objective is to
analyze and evaluate the performance of the GA in optimizing such robot
kinematic accuracy. In this algorithm, small changes in the kinematic
parameters values represent the parent and offspring population and the
end-effector error represents the fitness functions. A numerical
example has been used to demonstrate the convergence and effectiveness
of the given model.

This paper introduces a visual servoed manipulator controller based on fuzzy logic to guide a fabric towards a sewing machine. The task of the end-effector is to handle a randomly located fabric on a table and feed it to the sewing needle along the desired seam. The proposed fuzzy controller determines the linear and angular velocity of the end-effector taking into account the current position and orientation of the fabric which derive from the vision system. The fuzzy rules are derived after studying the behavior of human workers during sewing and the membership functions are formed after simulation and extended experimentation. The experimental results demonstrate the efficiency of the system as well as the robustness of the controller performance.