Skip to main content Accessibility help
×
Home

State adjustment of redundant robot manipulator based on quadratic programming

  • Kene Li (a1) and Yunong Zhang (a1)

Summary

To achieve desired configuration, a scheme for state adjustment of a redundant robot manipulator with no end-effector task explicitly assigned and referred to as a state-adjustment scheme is proposed in this paper. Owing to the physical limits in an actual robot manipulator, both joint and joint-velocity limits are incorporated into the proposed scheme for practical purposes. In addition, the proposed state-adjustment scheme is formulated as a quadratic program and resolved at the joint-velocity level. A numerical computing algorithm based on the conversion technique of the quadratic program to linear variational inequalities is presented to address the robot state-adjustment scheme. By employing the state-adjustment scheme, the robot manipulator can automatically move to the desired configuration from any initial configuration with the movement kept within its physical limits. Computer simulation and experimental results using a practical six-link planar robot manipulator with variable joint-velocity limits further verify the realizability, effectiveness, accuracy, and flexibility of the proposed state-adjustment scheme.

Copyright

Corresponding author

*Corresponding author. E-mail: ynzhang@ieee.org

References

Hide All
1.Sciavicco, L. and Siciliano, B., Modelling and Control of Robot Manipulators (Springer-Verlag, London, UK, 2000).
2.Meghdari, A., Naderi, D. and Eslami, S., “Optimal stability of a redundant mobile manipulator via genetic algorithm,” Robotica 24, 739743 (2006).
3.Zhang, Y., “On the LVI-based Primal-Dual Neural Network for Solving Online Linear and Quadratic Programming Problems,” Proceedings of the American Control Conference, Portland, OR, USA (2005) pp. 13511356.
4.Tang, W. S. and Wang, J., “A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architecture complexity,” IEEE Trans. Syst. Man Cybern. B 31 (1), 98105 (2001).
5.Puga, J. P. and Chiang, L. E., “Optimal trajectory planning for a redundant mobile manipulator with non-holonomic constraints performing push-pull tasks,” Robotica 26, 385394 (2008).
6.Ozbay, U., Sahin, H. T. and Zergeroglu, E., “Robust tracking control of kinematically redundant robot manipulators subject to multiple self-motion criteria,” Robotica 26 711728 (2008).
7.Allotta, B., Colla, V. and Bioli, G., “Kinematic control of robots with joint constraints,” ASME J. Dyn. Syst. Meas. Control 121 (3), 433442 (1999).
8.Mao, Z. and Hsia, T. C., “Obstacle avoidance inverse kinematics solution of redundant robots by neural networks,” Robotica 15, 310 (1997).
9.Zhang, Y. and Wang, J., “A dual neural network for constrained joint torque optimization of kinematically redundant manipulators,” IEEE Trans. Syst. Man Cybern. B 32 (5), 654662 (2002).
10.Zhang, Y., Wang, J. and Xu, Y., “A dual neural network for bi-criteria kinematic control of redundant manipulators,” IEEE Trans. Robot. Autom. 18 (6), 923931 (2002).
11.Zhang, Y., “A set of nonlinear equations and inequalities arising in robotics and its online solution via a primal neural network,” Neurocomputing 70, 513524 (2006).
12.Zhang, Y., Tan, Z., Yang, Z. and Lv, X., “A Dual Neural Network Applied to Drift-free Resolution of Five-Link Planar Robot Arm,” Proceedings of the 2008 IEEE International Conference on Information and Automation, Zhangjiajie, China (2008) pp. 12741279.
13.Donelan, P. S., “Singularity-theoretic methods in robot kinematics,” Robotica 25, 641659 (2007).
14.Padois, V., Fourquet, J.-Y. and Chiron, P., “Kinematic and dynamic model-based control of wheeled mobile manipulators: A unified framework for reactive approaches,” Robotica 25, 157173 (2001).
15.Lee, J., “A structured algorithm for minimum l -norm solutions and its application to a robot velocity workspace analysis,” Robotica 19, 343352 (2001).
16.Qiu, C., Cao, Q. and Miao, S., “An on-line task modification method for singularity avoidance of robot manipulators,” Robotica 27, 539546 (2009).
17.Wang, J., Hu, Q. and Jiang, D., “A Lagrangian network for kinematic control of redundant manipulators,” IEEE Trans. Neural Netw. 10 (5), 11231132 (1999).
18.Ding, H. and Tso, S. K., “A fully neural-network-based planning scheme for torque minimization of redundant manipulators,” IEEE Trans. Ind. Electron. 46 (1), 199206 (1999).
19.Ding, H. and Wang, J., “Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators,” IEEE Trans. Syst. Man Cybern. A 29 (3), 269276 (1999).
20.He, B., “Solving a class of linear projection equation,” Numer. Math. 168, 7180 (1994).
21.He, B., “A new method for a class of linear variational inequalities,” Math. Program. 166, 137144 (1994).
22.Wang, J., “Recurrent neural networks for computing pseudoinverses of rank-deficient matrices,” SIAM J. Sci. Comput. 18 (5), 14791493 (1997).
23.Zhang, Y., Chen, K. and Ma, W., “MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Online Solution of Linear Time-Varying Equations,” Proceedings of the 2007 International Conference on Life System Modeling and Simulation, Shanghai, China (2007) pp. 450454.
24.Zhang, Y., Ruan, G., Li, K. and Yang, Y., “Robustness analysis of the Zhang neural network for online time-varying quadratic optimization,” J. Phys. A-Math. Theor. 43, 119 (2010) (doi:10.1088/1751-8113/43/24/245202).

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed