<title>Compensation for robot arm flexibility using machine intelligence</title>

Abstract

This paper reports a new approach to error compensation for inaccuracies in position control for the end-effector of a Robot Arm. The goal is to overcome the problem of inaccuracy, due to the low precision in manufacturing of Robot Arms and the flexibility of their structure, by means of machine intelligence. Utilizing a mesh sensory system, a Real Time Monitoring System is designed. The position of the end-effector is monitored in real time and the positioning data for the end-effector is collected. A direction independent filtering system is designed to eliminate the noise from the collected data. After extracting the error map from the collected data, a novel Proportional Keen Approximation Method is implemented to generalize the error map. One of the main features of this method is the elimination of the training stage as in the Artificial Neural Networks. Using the knowledge obtained from the maps, the system compensates for the errors.