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Distributed control of modular and reconfigurable robot with torque sensing

Published online by Cambridge University Press:  01 January 2008

Guangjun Liu ,
Sajan Abdul  and
Andrew A. Goldenberg
Guangjun Liu*
Affiliation:
Department of Aerospace Engineering, Ryerson University, Canada.
Sajan Abdul
Affiliation:
Department of Aerospace Engineering, Ryerson University, Canada.
Andrew A. Goldenberg
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, Canada.
*
*Corresponding author. E-mail: gjliu@ryerson.ca.
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Summary

A major technical challenge in controlling modular and reconfigurable robots is associated with the kinematics and dynamic model uncertainties caused by reconfiguration. In parallel, conventional model uncertainties such as uncompensated joint friction still persist. This paper presents a modular distributed control technique for modular and reconfigurable robots that can instantly adapt to robot reconfigurations. Under the proposed control method that is based on joint torque sensing, a modular and reconfigurable robot is stabilized joint by joint, and modules can be added or removed without the need to adjust control parameters of the other modules of the robot. Model uncertainties associated with link and payload masses are compensated using joint torque sensor measurement. The remaining model uncertainties, including uncompensated dynamic coupling and joint friction, are compensated by a decomposition-based robust controller. Simulation results have confirmed the effectiveness of the proposed method.

Keywords

Decomposition-based controlRobust controlModular and reconfigurable robotFriction compensationJoint torque sensing
Type
Article
Information
Robotica , Volume 26 , Issue 1 , January 2008 , pp. 75 - 84
Copyright
Copyright © Cambridge University Press 2007

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References

1.Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E. and Chirikjian, G. S., “Modular self-reconfigurable robot systems—Challenges and opportunities for future,” IEEE Robot. Autom. Mag. 14 (1), 43–52 (2007).CrossRefGoogle Scholar
2.Fukuda, T. and Nakagawa, S., “Dynamically Reconfigurable Robotic System,” Procedings of the IEEE International Conference on Robotics and Automation 3 (1988) pp. 1581–86.Google Scholar
3.Chirikjian, G., “Kinematics of a Metamorphic Robotic System,” Proceedings IEEE International Conference Robotics and Automation 1 (1994) pp. 449–55.Google Scholar
4.Yim, M., Duff, D. G. and Roufas, K. D., “Polybot: A Modular Reconfigurable Robot,” Proceedings IEEE International Conference Robotics and Automation 1 (2000) pp. 514–20.Google Scholar
5.Tomita, K., Murata, S., Yoshida, E., Kurokawa, H. and Kokaji, S., “Reconfiguration method for a distributed mechanical system,” Distrib. Autonom. Robot. Syst. 2, 17–25 (1996).Google Scholar
6.Hui, R., Kircanski, N., Goldenberg, A. A., Zhou, C., Kuzan, P., Wiercienski, J., Gershon, D. and Sinha, P., “Design of the IRIS Facility—A Modular, Reconfigurable and Expandable Robot Test Bed,” Proceedings IEEE International Conference on Robotics and Automation 3 (1993) pp. 155–60.Google Scholar
7.Setchi, R. M. and Lagos, N., “Reconfigurability and Reconfigurable Manufacturing Systems: State-of-the-Art Review,” Proceedings IEEE International Conference of Industrial Informatics (2004) pp. 529–35.Google Scholar
8.Liu, G. and Goldenberg, A. A., “Robust control of robot manipulators based on dynamics decomposition,” IEEE Trans. Robot. Autom. 13 (5), 783–89 (1997).Google Scholar
9.Liu, G. and Goldenberg, A. A., “Uncertainty decomposition-based robust control of robot manipulators,” IEEE Trans. Control Sys. Technol. 4 (4), 384–93 (1996).CrossRefGoogle Scholar
10.Liu, G. and Goldenberg, A. A., “Comparative study of robust saturation control of robot manipulators: Analysis and experiments,” Int. J. Robot. Res. 15 (5), 473–91 (1996).Google Scholar
11.Melek, W. W. and Goldenberg, A. A., “Neurofuzzy control of modular and reconfigurable robots,” IEEE/ASME Trans. Mech. 8 (3), 381–89 (2003).Google Scholar
12.Melek, W. W. and Najjaran, H., “Study of the Effect of External Disturbances on the Position Control of IRIS Modular and Reconfigurable Manipulator,” IEEE International Conference on Mechatronics and Automation, 144–147 (2005).Google Scholar
13.Zhu, W.-H. and Lamarche, T., “Modular Robot Manipulators Based on Virtual Decomposition Control,” Proceedings IEEE International Conference Robotics and Automation, Roma, Italy, (2007) pp. 2235–2240.Google Scholar
14.Imura, J., Sugie, T., Yokokohji, Y. and Yoshikawa, T., “Robust Control of Robot Manipulators Based on Joint Torque Sensor Information,” Proceedings of IEEE/RSJ International Workshop on Intelligent Robots and Systems, Osaka, Japan, (1991) pp. 344–349.Google Scholar
15.Kosuge, K., Takeuchi, H. and Furuta, K., “Motion control of a robot arm using joint torque sensors,” IEEE Trans. Robot. Autom. 6 (2), 258–263 (1990).CrossRefGoogle Scholar
16.Liu, G., “Decomposition-based friction compensation of mechanical systems,” Mechatronics 12 (5), 755–769 (2002).Google Scholar
17.Armstrong-Helouvry, B., Dupont, P. and C. Canudas de Wit, “A survey of models, analysis tools and compensation methods for the control of machines with friction,” Automatica 30 (7), 1083–1138 (1994).CrossRefGoogle Scholar
18.Liu, G., Goldenberg, A. A. and Zhang, Y., “Precise slow motion control of a direct-drive robot arm with velocity estimation and friction compensation,” Mechatronics 14 (7), 821–834 (2004).CrossRefGoogle Scholar
19.Slotine, J. J. E. and Li, W., Applied nonlinear control (Prentice-Hall, Englewood Cliffs, NJ, 1991).Google Scholar