<title>Compensating for robot arm positioning inaccuracy in 3D space</title>

Abstract

This paper reports research in compensating for position inaccuracy and flexibility problems in a loosely coupled robot arm by means of machine learning methods. Error sources in the system are studied and problems are described. A number of methods for eliminating problems due to inaccuracy in 2D-space have been previously reported. These methods have been extended to address the problem in 3 dimensions. Utilizing a real time monitoring system, the end-effector position is sensed. The collected data is converted into appropriate error maps. Using a novel machine learning method, the error maps are used to predict system errors and compensate for them. The machine learning engine generalizes the data for the points between the sampled points. The experimental results are presented.