Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-02-02T22:22:13.964Z Has data issue: false hasContentIssue false
  • English
  • Français

Maximum load determination of nonholonomic mobile manipulator using hierarchical optimal control

Published online by Cambridge University Press:  26 April 2011

M. H. Korayem ,
V. Azimirad ,
H. Vatanjou  and
A. H. Korayem
M. H. Korayem*
Affiliation:
Robotic Research Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
V. Azimirad
Affiliation:
Robotic Research Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
H. Vatanjou
Affiliation:
Robotic Research Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. H. Korayem
Affiliation:
Robotic Research Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new method using hierarchical optimal control for path planning and calculating maximum allowable dynamic load (MADL) of wheeled mobile manipulator (WMM). This method is useful for high degrees of freedom WMMs. First, the overall system is decoupled to a set of subsystems, and then, hierarchical optimal control is applied on them. The presented algorithm is a two-level hierarchical algorithm. In the first level, interaction terms between subsystems are fixed, and in the second level, the optimization problem for subsystems is solved. The results of second level are used for calculating new estimations of interaction variables in the first level. For calculating MADL, the load on the end effector is increased until actuators get into saturation. Given a large-scale robot, we show how the presenting in distributed hierarchy in optimal control helps to find MADL fast. Also, it enables us to treat with complicated cost functions that are generated by obstacle avoidance terms. The effectiveness of this approach on simulation case studies for different types of WMMs as well as an experiment for a mobile manipulator called Scout is shown.

Keywords

Mobile manipulatorMaximum allowable dynamic loadHierarchical optimal controlObstacle avoidance
Type
Articles
Information
Robotica , Volume 30 , Issue 1 , January 2012 , pp. 53 - 65
Copyright
Copyright © Cambridge University Press 2011

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.Wang, C. E., Timoszyk, W. K. and Bobrow, J. E., “Payload maximization for open chained manipulators: Finding weightlifting motions for a puma 762 robot,” IEEE Trans. Robot. Autom. 17 (2), 218–224 (2001).CrossRefGoogle Scholar
2.Ghariblu, H. and Korayem, M. H., “Trajectory optimization of flexible mobile manipulators,” Robotica 24 (3), 333–335 (2006).CrossRefGoogle Scholar
3.Korayem, M. H., Nikoobin, A. and Azimirad, V., “Maximum load carrying capacity of mobile manipulators: Optimal control approach,” Robotica 27, 147–159 (2009).Google Scholar
4.Korayem, M. H., Azimirad, V., Nikoobin, A. and Boroujeni, Z., “Maximum load-carrying capacity of autonomous mobile manipulator in an environment with obstacle considering tip over stability,” Int. J. Adv. Manuf. Technol. 46, 811–830 (2010).CrossRefGoogle Scholar
5.Mohri, A., Furuno, S., Iwamura, M. and Yamamoto, M., “Sub-Optimal Trajectory Planning of Mobile Manipulator,” Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea 2 (2001) pp. 1271–1276.Google Scholar
6.Sadati, N. and Babazadeh, A., “Optimal control of robot manipulators with a new two-level gradient-based approach,” J. Electr. Eng. 88, 383–393 (2006).CrossRefGoogle Scholar
7.Sadati, N. and Emamzadeh, M. M., “Optimal Control of Robot Manipulators Using Fuzzy Interaction Prediction System,” Proceedings of the IEEE International Conference on Robotics and Biomimetics, Kunming (2006) pp. 1071–1076.Google Scholar
8.Papadopoulos, E., Poulakakis, I. and Papadimitriou, I., “On path planning and obstacle avoidance for nonholonomic mobile manipulators: a polynomial approach,” Int. J. Robot. Res. 21, 367–383 (2002).CrossRefGoogle Scholar
9.Gonzalez, V. J., Parkin, R., Para, M. L. and Dorador, J. M., “A wheeled mobile robot with obstacle avoidance capability”, Mech. Technologia 1, 150–159 (2004).Google Scholar
10.Chettibi, T., Lehtihet, H. E., Haddad, M. and Hanchi, S., “Minimum cost trajectory planning for industrial robots,” Eur. J. Mech. 23, 703–715 (2004).CrossRefGoogle Scholar
11.Jamshidi, M., Large-Scale Systems: Modeling and Control (North Holland, USA, 1983).Google Scholar
12.Wismer, D. A., Optimization Methods for Large-Scale Systems with Applications (McGraw Hill, USA, 1971).Google Scholar
13.Yamamoto, Y. and Yun, X., “Effect of the dynamic interaction on coordinated control of mobile manipulators,” IEEE Trans. Robot. Autom. 12 (5), 816–824 (1996).Google Scholar