The paper presents a preliminary study on the feasibility of a Neural Networks based methodology for the calibration of Industrial Manipulators to improve their accuracy. A Neural Network is used to predict the pose inaccuracy due to general sources of error in the robot (e.g. geometrical inaccuracy, load deflection, stiffness and backlash of the mechanical members, etc. . . ). The network is trained comparing the ideal model of the robot with measures of the actual poses reached by the robot. A back-propagation learning algorithm is applied. The Neural Network output can be used by the robot controller to compensate for the errors in the pose. The proposed calibration technique appears extremely simple. It does not need any information on the pose errors nature, but only the ideal robot kinematics and a set of experimental pose measures. Different schemes of calibration procedures are applied to a simulated SCARA robot and to a Stewart Platform and compared, in order to select the most suitable. Results of the simulations are presented and discussed.


    Access

    Download


    Export, share and cite



    Title :

    Improved Kinematics Calibration of Industrial Robots by Neural Networks



    Publication date :

    2010-01-01


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Inverse kinematics for industrial robots using conformal geometric algebra

    Kleppe, Adam Leon / Egeland, Olav | BASE | 2016

    Free access

    Kinematics of Robots and Mechanisms

    Kecskemethy, A. | British Library Conference Proceedings | 1996


    Metrology-integrated industrial robots : calibration, implementation and testing

    Kihlman, Henrik / Loser, Raimund / Cooke, Andrew et al. | BASE | 2004

    Free access

    Kinematics and Dynamics of Space Robots

    Wang, Yaobing | Springer Verlag | 2020