Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-21T18:04:40.518Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic modeling and stability optimization of a redundant mobile robot using a genetic algorithm

Published online by Cambridge University Press:  26 July 2011

M. Mosadeghzad ,
D. Naderi  and
S. Ganjefar
M. Mosadeghzad*
Affiliation:
Engineering Faculty, Bu Ali-Sina University, Hamedan, Iran
D. Naderi
Affiliation:
Engineering Faculty, Bu Ali-Sina University, Hamedan, Iran
S. Ganjefar
Affiliation:
Engineering Faculty, Bu Ali-Sina University, Hamedan, Iran
*
*Corresponding author: E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Kinematic reconfigurable mobile robots have the ability to change their structure to increase stability and decrease the probability of tipping over on rough terrain. If stability increases without decreasing center of mass height, the robot can pass more easily through bushes and rocky terrain. In this paper, an improved sample return rover is presented. The vehicle has a redundant rolling degree of freedom. A genetic algorithm utilizes this redundancy to optimize stability. Parametric motion equations of the robot were derived by considering Iterative Kane and Lagrange's dynamic equations. In this research, an optimal reconfiguration strategy for an improved SRR mobile robot in terms of the Force–Angle stability measure was designed using a genetic algorithm. A path-tracking nonlinear controller, which maintains the robot's maximum stability, was designed and simulated in MATLAB. In the simulation, the vehicle and end-effector paths and the terrain are predefined and the vehicle has constant velocity. The controller was found to successfully keep the end-effector to the desired path and maintained optimal stability. The robot was simulated using ADAMS for optimization evaluation.

Keywords

Mobile manipulatorDynamic modelingKane dynamicsLagrange dynamicsStabilityGenetic algorithmNonlinear control
Type
Articles
Information
Robotica , Volume 30 , Issue 3 , May 2012 , pp. 505 - 514
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.Lagnemma, K., Rzepniewski, A., Dubowsky, S., Pirjanian, P., Huntsberger, T. and Schenker, P., “Mobile Robot Kinematic Reconfigurability for Rough-Terrain”, Proceedings of the SPIE, Boston, MA, USA (Aug. 2000) vol. 4196, pp. 413–420.Google Scholar
2.Kane, T. R. and Levinson, D. A., “The use of Kane's dynamical equations in robotics,” Int. J. Robot. Res. 2 (3), 3–21 (1983).CrossRefGoogle Scholar
3.Sharifi, M., Mahalingam, S. and Dwivedi, S., “Derviation of Kane's Dynamical Equations for a Three Link (3R) Manipulator,” Proceedings of the IEEE Twentieth Southeastern Symposium on System Theory, Charlotte, NC, USA (1988) vol. 1, pp. 573–580.CrossRefGoogle Scholar
4.Nukulwauthiopas, W., Laowattana, S. and Maneewarn, T., “Dynamic Modeling of a One-Wheel Robot by Using Kane's Method,” Proceedings of the IEEE International Conference on Industrial Technology (ICIT '02), Bangkok, Thailand (2002) vol. 1, pp. 524–529.Google Scholar
5.Thanjavur, K. and Rajagopalan, R., “Ease of Dynamic Modeling of Wheeled Mobile Robots (WMRs) using Kane's Approach,” Proceedings of the IEEE International Conference on Robotic and Automation, Albuquerque, New Mexico (1997) pp. 2926–2931.CrossRefGoogle Scholar
6.Tanner, H. G. and Kyriakopoulos, K. J., “Mobile manipulator modeling with Kane's approach,” Robotica 19, 675–690 (2001).CrossRefGoogle Scholar
7.Ghafari, A., Meghdari, A., Naderi, D. and Eslami, S., “Stability enhancement of mobile manipulator via soft computing,” Int. J. Adv. Robot. Syst. 3 (3), 191–198 (2006).Google Scholar
8.Papadopoulos, E. G. and Rey, D. A., “A New Measure of Tipover Stability Margin for Mobile Manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA (Apr. 1996).Google Scholar
9.Suza, D. and Frank, A., Advanced Dynamics: Modeling and Analysis (Prentice-Hall, New Jersey, NJ, USA, 1984).Google Scholar
10.Kane, R. T., Levinson, S. and Maneewarn, A. D., Dynamics: Theory and Applications (McGraw-Hill, New York, 1985).Google Scholar
11.Craig, J. J., Introduction to Robotics: Mechanics and Control, 3rd ed. (Pearson/Prentice Hall, NJ, USA, 2005).Google Scholar
12.Sreenivasan, S. and Wilcox, B., “Stability and traction control of an actively actuated micro-rover,” J. Robot. Syst. 11 (6), 487–502 (1994).CrossRefGoogle Scholar
13.Sreenivasan, S. and Waldron, K., “Displacement analysis of an actively articulated wheeled vehicle configuration with extensions to motion planning on uneven terrain,” ASME J. Mech. Des. 118 (2), 312–317 (1996).CrossRefGoogle Scholar
14.Farritor, S., Hacot, H. and Dubowsky, S., “Physics-Based Planning for Planetary Exploration,” Proceedings of the 1998 IEEE International Conference on Robotics and Automation, Belgium (May 1998).Google Scholar
15.Hoorfar, A., “A Comparative Study of Corrugated Horn Design by Evolutionary Technique,” Proceedings of the 2003 IEEE Aerospace Conference, Big Sky, MT, USA (Mar 2003).Google Scholar