Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T07:37:04.641Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of joint error maximum mutual compensation for hexapod robots

Published online by Cambridge University Press:  01 January 2008

Yauheni Veryha  and
Henrik Gordon Petersen
Yauheni Veryha*
Affiliation:
Department of Industrial Software and Applications, ABB Corporate Research, 68526, Ladenburg, Germany.
Henrik Gordon Petersen
Affiliation:
The Maersk Mc-Kinney Moller Institute for Production Technology, University of Southern Denmark, 5230 Odense M.
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

A good practice to ensure high-positioning accuracy in industrial robots is to use joint error maximum mutual compensation (JEMMC). This paper presents an application of JEMMC for positioning of hexapod robots to improve end-effector positioning accuracy. We developed an algorithm and simulation framework in MatLab to find optimal hexapod configurations with JEMMC. Based on a real hexapod model, simulation results of the proposed approach are presented. Optimal hexapod configurations were found using the local minimum of the infinity norm of hexapod Jacobian inverse. JEMMC usage in hexapod robots can improve hexapod end-effector positioning accuracy by two times and more.

Keywords

HexapodRobot calibrationPositioning accuracyJoint error maximum mutual compensationWorst-case error
Type
Article
Information
Robotica , Volume 26 , Issue 1 , January 2008 , pp. 63 - 73
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Andreff, N., Marchadier, A. and Martinet, P., “Vision-based control of a Gough-Stewart parallel mechanism using legs observation”, Proceeding the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005), pp. 2546–2551.Google Scholar
2.Angeles, J., Fundamentals of Robotic Mechanical Systems. (Springer, New York, USA, 2003).Google Scholar
3.Bai, S. and Teo, M., “Kinematic calibration and pose measurement of a medical parallel manipulator by optical position sensors”, J. Robot. Syst. 20 (4), 201–209 (2003).CrossRefGoogle Scholar
4.Besnard, S. and Khalil, W., “Identifiable parameters for parallel robots kinematic calibration”, Proceeding of IEEE International Conference on Robotics and Automation (Seoul, South Korea (2001) pp. 2859–2866.Google Scholar
5. K-S., Chai, Young, K. and Tuersley, I., “A practical calibration process using partial information for a commercial Stewart platform”, Robotica, 20 (3), 315–322, (2002).Google Scholar
6.Chong, E. and Zak, S., An Introduction to Optimization. (Wiley, New York, USA, 2001).Google Scholar
7.Daney, D. and Emiris, I., “Robust parallel robot calibration with partial information”, Proceeding of IEEE International Conference on Robotics and Automation, Seoul, South Korea (2001) pp. 3262–3267.Google Scholar
8.Daney, D., “Kinematic calibration of the Gough platform”, Robotica, 21 (6), 677–690 (2003).CrossRefGoogle Scholar
9.Daney, D., Papegay, Y. and Neumaier, A., “Interval methods for certification of the kinematic calibration of parallel robots”, Proceeding of IEEE International Conference on Robotics and Automation, New Orleans, USA (2004), pp. 1913–1918.Google Scholar
10.Daney, D., Papegay, Y. and Madeline, B., “Choosing measurement poses for robot calibration with the local convergence method and Tabu search”, Int. J. Robot. Research, 24 (6), 501–518 (2005).CrossRefGoogle Scholar
11.Doering, K., “Control and coordination of robots in a flexible multi-robot platform with a special reference to micro-handling”, Industrial Ph.D. Dissertation, (University of Southern Denmark, Odense, Denmark, 2004).Google Scholar
12.Iurascu, C. and Park, F., “Geometric algorithms for closed chain kinematic calibration”, Proceedind of IEEE Conference on Robotics and Automation, Detroit, USA (1999) pp. 1752–1757.Google Scholar
13.Ji, Z. and Li, Z., “Identification of placement parameters for modular platform manipulators”, J. Robot. Sys. 16 (4), 227–236 (1999).3.0.CO;2-J>CrossRefGoogle Scholar
14.Khalil, W. and Besnard, S., “Self calibration of Stewart-Gough parallel robot without extra sensors”, IEEE Trans. Robot. Autom. 15 (6), 1116–1121 (1999).CrossRefGoogle Scholar
15.Nahvi, A., Hollerbach, J. and Hayward, V., “Calibration of a parallel robot using multiple kinematics closed loops”, Proceeding of IEEE International Conference on Robotics and Automation, San Diego, USA (1994) pp. 407–412.Google Scholar
16.Rauf, A. and Ryu, J., “Fully autonomous calibration of parallel manipulators by imposing position constraint”, Proceeding of IEEE International Conference on Robotics and Automation, Seoul, South Korea (2001), pp. 2389–2394.Google Scholar
17.Renaud, P., Andreff, N., Pierrot, F. and Martinet, P., “Combining end-effector and legs observation for kinematic calibration of parallel mechanisms”, Proceeding of IEEE International Conference on Robotics and Automation, New Orleans, USA (2004) pp. 4116–4121.Google Scholar
18.Veryha, Y., Positioning of Robotic Manipulator End-Effector Using Joint Error Maximum Mutual Compensation. (Logos-Verlag, Berlin, Germany, 2006).Google Scholar
19.Veryha, Y. and Kurek, J., “Application of joint error mutual compensation for robot end-effector pose accuracy improvement”, J. Intell. Robot. Syst. 36 (3), 315–329, (2003).CrossRefGoogle Scholar
20.Veryha, Y. and Kurek, J., “Robotic manipulator pose accuracy improvement using joint error maximum compensation,” Proceeding of IEEE International Conference on Methods and Models in Automation and Robotics, Szczecin, Poland (2002) pp. 993–998.Google Scholar
21.Yiu, Y., Meng, J. and Li, Z., “Auto-calibration for a parallel manipulator with sensor redundancy,” Proceeding of IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 3660–3665.Google Scholar
22.Zhuang, H. and Roth, Z., “Method for kinematic calibration of Stewart platforms”, J. Robo. Syst. 10 (3), 391–405 (1993).CrossRefGoogle Scholar
23.Zhuang, H., Yan, J. and Masory, O., “Calibration of Stewart platform and other parallel manipulators by minimizing inverse kinematic residuals”, J. Robot. Syst. 15 (7), 396–406, (1998).3.0.CO;2-H>CrossRefGoogle Scholar