Advertisement

Application to Fixed-Base Robot RMP

  • Yunong ZhangEmail author
  • Dongsheng Guo
Chapter
  • 511 Downloads

Abstract

In this chapter, the ZD approach presented in the previous chapters is applied to repetitive motion planning (RMP) of fixed-base redundant robot manipulators at the joint-acceleration level. Specifically, by introducing two different ZFs and by exploiting the ZD design formula, an acceleration-level RMP performance index is proposed, developed, and investigated. The resultant RMP scheme, which incorporates joint-angle, joint-velocity and joint-acceleration limits, is further presented and investigated to remedy the joint-angle drift phenomenon of fixed-base redundant robot manipulators. Such a scheme is then reformulated as a quadratic program, which is solved by a primal–dual neural network. With three path-tracking examples, simulation results based on PUMA560 robot manipulator substantiate well the effectiveness and accuracy of the proposed acceleration-level RMP scheme, as well as show the application prospect of the presented ZD approach.

Keywords

Quadratic Program Robot Manipulator Joint Velocity Task Duration Inverse Kinematic Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Janabi-Sharifi F, Hassanzadeh I (2011) Experimental analysis of mobile-robot teleoperation via shared impedance control. IEEE Trans Syst Man Cybern B 41(2):591–606Google Scholar
  2. 2.
    Su J, Xie W (2011) Motion planning and coordination for robot systems based on representation space. IEEE Trans Syst Man Cybern B 41(1):248–299MathSciNetGoogle Scholar
  3. 3.
    Guo D, Zhang Y (2014) Li-function activated ZNN with finite-time convergence applied to redundant-manipulator kinematic control via time-varying Jacobian matrix pseudoinversion. Appl Soft Comput 24:158–168Google Scholar
  4. 4.
    Sun C, Xu WL, Bronlund JE, Morgenstern M (2014) Dynamics and compliance control of a linkage robot for food chewing. IEEE Trans Ind Electron 61(1):377–386Google Scholar
  5. 5.
    Guo D, Zhang Y (2014) Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators. IEEE Trans Ind Electron 61(12):6903–6914Google Scholar
  6. 6.
    Zhang Y, Zhang Z (2013) Repetitive motion planning and control of redundant robot manipulators. Springer, New YorkCrossRefzbMATHGoogle Scholar
  7. 7.
    Okadome Y, Nakamura Y, Ishiguro H (2014) Predictive control method for a redundant robot using a non-parametric predictor. Adv Robot 28(10):647–657Google Scholar
  8. 8.
    Zhang Y, Wang J, Xia Y (2003) A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits. IEEE Trans Neural Netw 14(3):658–667Google Scholar
  9. 9.
    Zhang Z, Zhang Y (2012) Acceleration-level cyclic-motion generation of constrained redundant robots tracking different paths. IEEE Trans Syst Man Cybern B 42(4):1257–1269Google Scholar
  10. 10.
    Guo D, Zhang Y (2012) A new inequality-based obstacle-avoidance MVN scheme and its application to redundant robot manipulators. IEEE Trans Syst Man Cybern C 42(6):1326–1340Google Scholar
  11. 11.
    Maciekewski AA, Klein CA (1985) Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Int J Robot Res 4(3):109–117Google Scholar
  12. 12.
    Cheng FT, Sheu RJ, Chen TH (1995) The improved compact QP method for resolving manipulator redundancy. IEEE Trans Syst Man Cybern 25(11):1521–1530Google Scholar
  13. 13.
    Zhang Z, Zhang Y (2013) Variable joint-velocity limits of redundant robot manipulators handled by quadratic programming. IEEE/ASME Trans Mech 18(2):674–686Google Scholar
  14. 14.
    Tchon K, Jakubiak J (2005) A repeatable inverse kinematics algorithm with linear invariant subspaces for mobile manipulators. IEEE Trans Syst Man Cybern B 35(5):1051–1057Google Scholar
  15. 15.
    Siciliano B, Khatib O (2008) Springer handbook of robotics. Springer, HeidelbergCrossRefzbMATHGoogle Scholar
  16. 16.
    Siciliano B, Sciavicco L, Villani L, Oriolo G (2009) Robotics: modelling, planning and control. Springer, LondonCrossRefGoogle Scholar
  17. 17.
    Zhang Y, Tan Z, Chen K, Yang Z, Lv X (2009) Repetitive motion of redundant robots planned by three kinds of recurrent neural networks and illustrated with a four-link planar manipulator’s straight-line example. Robot Auton Syst 57(6–7):645–651Google Scholar
  18. 18.
    Zhang Y, Lv X, Li Z, Yang Z, Chen K (2008) Repetitive motion planning of PA10 robot arm subject to joint physical limits and using LVI-based primal-dual neural network. Mechatronics 18(9):475–485CrossRefGoogle Scholar
  19. 19.
    Cai B, Zhang Y (2010) Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural network. Optim Control Appl Methods 31(3):213–229zbMATHMathSciNetGoogle Scholar
  20. 20.
    Roberts RG, Maciejewski AA (1993) Repeatable generalized inverse control strategies for kinematically redundant manipulators. IEEE Trans Autom Control 38(5):689–698zbMATHMathSciNetGoogle Scholar
  21. 21.
    Shamir T, Yomdin Y (1988) Repeatability of redundant manipulators: mathematical solution of the problem. IEEE Trans Autom Control 33(11):1004–1009zbMATHMathSciNetGoogle Scholar
  22. 22.
    Ge SS, Zhang Y, Lee TH (2004) An acceleration-based weighting scheme for minimum-effort inverse kinematics of redundant manipulators. In: Proceedings of the IEEE international symposium on intelligent control, pp 275–280Google Scholar
  23. 23.
    Zhang Y, Yi C (2011) Zhang neural networks and neural-dynamic method. Nova Science Publishers, New YorkGoogle Scholar
  24. 24.
    Xue F, Hou Z, Deng H (2011) Balance control for an acrobat. In: Proceedings of the IEEE international conference on control decision, pp 3426–3429Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Information Science and TechnologySun Yat-sen UniversityGuangzhouChina

Personalised recommendations