Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T15:03:59.359Z Has data issue: false hasContentIssue false

Physical-limits-constrained minimum velocity norm coordinating scheme for wheeled mobile redundant manipulators

Published online by Cambridge University Press:  01 April 2014

Yunong Zhang*
Affiliation:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China Research Institute of Sun Yat-sen University in Shenzhen, Shenzhen 518057, China
Weibing Li
Affiliation:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China
Zhijun Zhang
Affiliation:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China
*
*Corresponding author. Emails: zhynong@mail.sysu.edu.cn, jallonzyn@sina.com

Summary

In order to resolve the redundancy of a wheeled mobile redundant manipulator comprising a two-wheel-drive mobile platform and a 6-degree-of-freedom manipulator, a physical-limits-constrained (PLC) minimum velocity norm (MVN) coordinating scheme (termed as PLC-MVN-C scheme) is proposed and investigated. Such a scheme can not only coordinate the mobile platform and the manipulator to fulfill the end-effector task and to achieve the desired optimal index (i.e., minimizing the norm of the rotational velocities of the wheels and the joint velocities of the manipulator) but also consider the physical limits of the robot (i.e., the joint-angle limits and joint-velocity limits of the manipulator as well as the rotational velocity limits of the wheels). The scheme is then reformulated as a quadratic program (QP) subject to equality and bound constraints, and is solved by a discrete QP solver, i.e., a numerical algorithm based on piecewise-linear projection equations (PLPE). Simulation results substantiate the efficacy and accuracy of such a PLC-MVN-C scheme and the corresponding discrete PLPE-based QP solver.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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. Halatci, I., Brooks, C. A. and Iagnemma, K., “A study of visual and tactile terrain classification and classifier fusion for planetary exploration rovers,” Robotica 26 (6), 767779 (2008).Google Scholar
2. Xu, W., Liang, B., Li, C. and Xu, Y., “Autonomous rendezvous and robotic capturing of non-cooperative target in space,” Robotica 28 (5), 705718 (2010).Google Scholar
3. Lee, S., Yu, S., Yu, S. and Han, C., “An improved multipurpose field robot for installing construction materials,” Robotica 28 (7), 945957 (2010).Google Scholar
4. Garcia, C., Saltaren, R. and Aracil, R., “Experiences in the development of a teleoperated parallel robot for aerial line maintenance,” Robotica 29 (29), 873881 (2011).Google Scholar
5. Ma, W., Yan, W., Fu, Z. and Zhao, Y., “A Chinese cooking robot for elderly and disabled people,” Robotica 29 (06), 843852 (2011).Google Scholar
6. Nakamura, Y. and Mukherjee, R., “Nonholonomic Path Planning of Space Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Scottsdale, USA (1989) pp. 10501055.Google Scholar
7. Umetani, Y. and Yoshida, K., “Resolved motion rate control of space manipulators with generalized Jacobian matrix,” IEEE/ASME Trans. Robot. Autom. 5 (3), 303314 (1989).Google Scholar
8. Vafa, Z. and Dubowsky, S., “The kinematics and dynamics of space manipulators: The virtual manipulator approach,” Int. J. Robot. Res. 9 (4), 321 (1990).Google Scholar
9. Liu, K. and Lewis, F. L., “Decentralized Continuous Robust Controller for Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, USA (1990) pp. 18221827.Google Scholar
10. Campion, G., Bastin, G. and Dandrea-Novel, B., “Structural properties and classification of kinematic and dynamic models of wheeled mobile robots,” IEEE Trans. Robot. Autom. 12 (1), 4762 (1996).Google Scholar
11. Huang, Q., Tanie, K. and Sugano, S., “Coordinated motion planning for a mobile manipulator considering stability and manipulation,” Int. J. Robot. Res. 19 (8), 732742 (2000).Google Scholar
12. Bayle, B., Fourquet, J. Y. and Renaud, M., “Manipulability of wheeled mobile manipulators: application to motion generation,” Int. J. Robot. Res. 22 (7–8), 565581 (2003).Google Scholar
13. De Luca, A., Oriolo, G. and Giordano, P. R., “Kinematic Modeling and Redundancy Resolution for Nonholonomic Mobile Manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, USA (2006) pp. 18671873.Google Scholar
14. Klein, C. A. and Ahmed, S., “Repeatable pseudoinverse control for planar kinematically redundant manipulators,” IEEE Trans. Syst., Man Cybern. 25 (12), 16571662 (1995).Google Scholar
15. Cheng, F., Chen, T. and Sun, Y., “Resolving manipulator redundancy under inequality constraints,” IEEE/ASME Trans. Rob. Autom. 10 (1), 6571 (1994).Google Scholar
16. Zhang, Y., Tan, Z., Chen, K., Yang, Z. and Lv, X., “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), 645651 (2009).Google Scholar
17. Ma, S., Kobayashi, I., Hirose, S. and Yokoshima, K., “Control of a multijoint manipulator ‘Moray Arm’,” IEEE/ASME Trans. Mechatron. 7 (3), 304317 (2002).Google Scholar
18. Zhang, Y., Wang, J. and Xia, Y., “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), 658667 (2003).Google Scholar
19. Lu, Y., Shi, Y. and Yu, J., “Determination of singularities of some 4-DOF parallel manipulators by translational/rotational Jacobian matrices,” Robotica 28, 811819 (2009).Google Scholar
20. Siqueira, A. A. G. and Terra, M. H., “A fault-tolerant manipulator robot based on H 2, H , and mixed H 2/H Markovian controls,” IEEE/ASME Trans. Mechatron. 14 (2), 257263 (2009).Google Scholar
21. Andruska, A. M. and Peterson, K. S., “Control of a snake-like robot in an elastically deformable channel,” IEEE/ASME Trans. Mechatron. 13 (2), 219227 (2008).Google Scholar
22. Xu, D., Hu, H., Calderon, C. A. A. and Tan, M., “Motion Planning for a Mobile Manipulator with Redundant DOFs,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systmes, Las Vegas, USA (1990) pp. 110.Google Scholar
23. Cui, Z., Cao, P., Shao, Y., Qian, D. and Wang, X., “Trajectory Planning for a Redundant Mobile Manipulator Using Avoidance Manipulability,” Proceedings of the IEEE International Conference on Automation and Logistics, Shenyang, China (2009) pp. 283288.Google Scholar
24. Ding, C., Duan, P., Zhang, M. and Liu, H., “The Kinematics Analysis of a Redundant Mobile Manipulator,” Proceedings of the IEEE International Conference on Automation and Logistics, Qingdao, China (2008) pp. 23522357.Google Scholar
25. Siciliano, B. and Khatib, O., Springer Handbook of Robotics (Springer-Verlag, Heidelberg, Germany, 2008).Google Scholar
26. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer, London, England, 2010).Google Scholar
27. Siegwart, R. and Nourbakhsh, I. R., Introduction to Autonomous Mobile Robots (The MIT Press, Cambridge, MA, USA, 2004).Google Scholar
28. Zhang, Y., Analysis and design of recurrent neural networks and their applications to control and robotic systems, Ph.D. Thesis (Chinese University of Hong Kong, 2002).Google Scholar
29. Zhang, Y., Guo, D., Cai, B. and Chen, K., “Remedy scheme and theoretical analysis of joint-angle drift phenomenon for redundant robot manipulators,” Robot. Comput. Integrated Manuf. 27 (4), 860869 (2011).Google Scholar
30. He, B., “A new method for a class of linear variational inequalities,” Math. Program. 66 (1–3), 137144 (1994).Google Scholar
31. Zhang, Z. and Zhang, Y., “Variable joint-velocity limits of redundant robot manipulators handled by quadratic programming,” IEEE/ASME Trans. Mechatron. 18 (2), 674686 (2013).Google Scholar
32. Zhang, Y., Li, W., Yu, X., Wu, H. and Li, J., “Encoder based online motion planning and feedback control of redundant manipulators,” Control Eng. Practice 21 (10), 12771289 (2013).Google Scholar
33. Zemcik, P., “Hardware Acceleration of Graphics and Imaging Algorithms Using FPGAs,” Proceedings of the 18th spring conference on Computer graphics, Budmerice, Slovakia (2002) pp. 2532.Google Scholar
34. Xue, X., Cheryauka, A. and Tubbs, D., “Acceleration of fluoro-CT reconstruction for a mobile C-arm on GPU and FPGA hardware: A simulation study,” Proc. SPIE 6142, 14941501 (2006).Google Scholar
35. Cai, Z., Robotics (Tsinghua University Press, Beijing, China, 2009).Google Scholar
36. Chitta, S., Cohen, B. and Likhachev, M., “Planning for Autonomous Door Opening with a Mobile Manipulator,” Proceedings of the International Conference on Robotics and Automation, Anchorage, Alaska (2010) pp. 17991806.Google Scholar
37. Theoretical Mechanics Educational Research Group of Harbin Institute of Technology, Theoretical Mechanics, 5th ed. (Higher Education Press, Harbin, China, 2004).Google Scholar
38. Yamamoto, Y. and Yun, X., “Coordinating locomotion and manipulation of a mobile manipulator,” IEEE Trans. Autom. Control 39 (6), 13261332 (1994).Google Scholar