Improved Kinematics Calibration of Industrial Robots by Neural Networks

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 out- put 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 er- rors 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 Plat- form and compared, in order to select the most suitable. Results of the simulations are presented and discussed.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Tosi Diego,et al.  A closed-loop neuro-parametric methodology for the calibration of a 5 DOF measuring robot , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[3]  Mark R. Cutkosky,et al.  Modeling manufacturing grips and correlations with the design of robotic hands , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[4]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[5]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[6]  Giovanni Legnani,et al.  Three methodologies for the calibration of industrial manipulators: Experimental results on a SCARA robot , 2000 .

[7]  Dan Ding,et al.  Computing 3-D optimal form-closure grasps , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Zvi S. Roth,et al.  Fundamentals of Manipulator Calibration , 1991 .

[9]  Farrokh Sassani,et al.  Kinematic Calibration of Industrial Hydraulic Manipulators , 1996, Robotica.

[10]  周建平,et al.  Strain analysis of nonlocal viscoelastic Kelvin bar in tension , 2008 .

[11]  Giovanni Legnani,et al.  Static calibration of industrial manipulators: Design of an optical instrumentation and application to SCARA robots , 1996, J. Field Robotics.

[12]  J. Ziegert,et al.  Basic considerations for robot calibration , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[13]  Müjdat Çetin,et al.  A criteria-based approach to grasp synthesis , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[14]  George A. Bekey,et al.  Intelligent Learning for Deformable Object Manipulation , 1999, Auton. Robots.

[15]  Reymond Clavel,et al.  Calibration of the 6 DOF High-Precision Flexure Parallel Robot "Sigma 6" , 2006 .

[16]  Y. B. Kavina,et al.  Research. An overview of robot calibration techniques , 1988 .

[17]  Antonio Morales,et al.  Vision-based three-finger grasp synthesis constrained by hand geometry , 2006, Robotics Auton. Syst..