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Development of a two-wheel mobile manipulator: balancing and interaction control

Published online by Cambridge University Press:  16 January 2014

Jae Kook Ahn  and
Seul Jung
Jae Kook Ahn
Affiliation:
Intelligent Systems and Emotional Engineering Laboratory, Department of Mechatronics Engineering, Chungnam National University, Dajeon, Korea
Seul Jung*
Affiliation:
Intelligent Systems and Emotional Engineering Laboratory, Department of Mechatronics Engineering, Chungnam National University, Dajeon, Korea
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper focuses on practical application of a mobile manipulator by presenting the development and control of a two-wheel mobile robot with two arms called a balancing service robot (BSR) designated for indoor services. The mobile manipulator requires not only robust balancing position control but also force control to interact with objects. Movements with two wheels are controlled to satisfy stable balancing control for navigation and manipulation with two arms to perform given tasks. The robot is required to deal with external forces to maintain balance. The position-based impedance force control method (the admittance control) is utilized by filtering the force with the impedance function to react to the applied force from the operator. Experimental studies of navigation control under balancing condition and interacting control with a human operator are demonstrated. Experimental results confirm that the robot has smooth reaction against the disturbance induced by the applied external force.

Keywords

Control of robotic systemsForce controlService robotsMobile robotsMechatronic systems
Type
Articles
Information
Robotica , Volume 32 , Issue 7 , October 2014 , pp. 1135 - 1152
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Ambrose, R. O., Savely, R. T., Goza, S. M, Strawser, P., Diftler, M. A., Spain, I. and Radford, N., “Mobile Manipulation Using NASA's Robonaut,” IEEE ICRA, New Orleans, USA (2004) pp. 2104–2109.Google Scholar
2.Jeong, S. H. and Takayuki, T., “Wheeled Inverted Pendulum Type Assistant Robot: Design Concept and Mobile Control,” IEEE IROS, San Diego, USA (2007) pp. 1932–1937.Google Scholar
3.Abeygunawardhana, P. K. and Murakami, T., “Environmental Interaction of Two Wheeled Mobile Manipulator by Using Reaction Torque Observer,” IEEE Workshop on Advanced Motion Control, Trento, Italy (2008) pp. 348–353.Google Scholar
4.Ding, C., Duan, P., Zhang, M. and Liu, H., “The Kinematics Analysis of a Redundant Mobile Manipulator,” IEEE Conference on Automations and Logistics, Qingdao, China (2008) pp. 2352–2357.Google Scholar
5.Wu, Y. and Hu, Y., “Output Tracking Control of Mobile Manipulator Via Dynamical Sliding Mode Control,” IEEE Conference on Mechatronics and Automation, Niagara Falls, Canada (2005) pp. 2160–2164.Google Scholar
6.White, G. D., Bhatt, R. M., Tang, C. P. and Krovi, V. N., “Experimental evaluation of dynamic redundancy resolution in a non-holonomic wheeled mobile manipulator,” IEEE/ASME Trans. Mechatronics 14 (3), 349–357 (2009).CrossRefGoogle Scholar
7.Xu, D., Zhao, D., Yi, J. and Tan, X., “Trajectory tracking control of omni-directional wheeled mobile manipulators: robust neural network-based sliding mode approach,” IEEE Trans. Syst. Man Cybern. 39 (3), 788–799 (2009).Google Scholar
8.Tan, X., Zhao, D., Yi, J., Hou, Z. G. and Xu, D., “Unified Model and Robust Neural Network Control of Omni-Directional Mobile Manipulators,” IEEE Conference on Cognitive Informatics, Lake Tahoe, USA (2007) pp. 411–418.CrossRefGoogle Scholar
9.Sasaki, K. and Murakami, T., “Pushing Operation by Two-Wheel Inverted Mobile Manipulator,” IEEE Workshop on Advanced Motion Control, Trento, Italy (2008) pp. 33–37.Google Scholar
10.Chen, Y., Liu, L., Zhang, M. and Rong, H., “Study on Coordinated Control and Hardware System of a Mobile Manipulator,” World Congress on Intelligent Control and Automation, Dalian, China (2006) pp. 9037–9041.Google Scholar
11.Li, Z., Ge, S. S. and Wang, Z., “Robust adaptive control of coordinated multiple mobile manipulators,” IEEE Conference on Control Applications, Singapore (2007) pp.71–76.Google Scholar
12.Iwata, H. and Sugano, S., “Design of Human Symbiotic Robot TWENDY-ONE,” IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 580–586.Google Scholar
13.Chung, W., Rhee, C., Shim, Y., Lee, H. and Park, S., “Door opening control of a service robot using a multi fingered robot hand,” IEEE Trans. Ind. Electron. 56 (10), 3975–3984 (2009).CrossRefGoogle Scholar
14.Jeon, P. W., Jung, S. and Hsia, T. C., “Contour tracking of an unknown planar object by regulating force for mobile robot navigation” Robotica 25 (3), 297–305 (2008).Google Scholar
15.Park, J., Hyun, D., Cho, W. H., Kim, T. H. and Yang, H. S., “Normal force control for an in-pipe robot according to the inclination of the pipelines,” IEEE Trans. Ind. Electron. 58 (12), 5304–5310 (2011).CrossRefGoogle Scholar
16.Kiguchi, K. and Fukuda, T., “Position/force control of robot manipulators for geometrically unknown objects using fuzzy neural networks,” IEEE Trans. Ind. Electron. 47 (3), 641–649 (2000).CrossRefGoogle Scholar
17.Bonitz, R. and Hsia, T., “Robust internal force-based impedance control for coordinating manipulators - theory and experiments,” Int. J. Intell. Mechatronics 4 (1), 35–67 (Jan. 1999).Google Scholar
18.Bonitz, R. and Hsia, T., “Internal force-based impedance control for cooperating manipulators,” IEEE Trans. Robot. Autom. 12 (1), 78–89 (Feb. 1996).CrossRefGoogle Scholar
19.Wang, Z. D., Hirata, Y. and Kosuge, K., “Impedance-based Motion Control of Passive-type Robot Porter for Handling an Object,” Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China (2006) pp.709–714.Google Scholar
20.Zhang, J. and Knoll, A., “A two-arm situated artificial communicator for human-robot cooperative assembly,” IEEE Trans. Ind. Electron. 50 (4), 651–658 (2003).CrossRefGoogle Scholar
21.Grunwald, G., Schreiber, G., Albu-Schaffer, A. and Hirzinger, G., “Programming by touch: The different way of human-robot interaction,” IEEE Trans. Ind. Electron. 50 (4), 659–666 (2003).CrossRefGoogle Scholar
22.Iwata, H. and Sugano, S., “Human-robot contact state identification based on tactile recognition“, IEEE Trans. Ind. Electron. 52 (6), 1468–1477 (2005).CrossRefGoogle Scholar
23.Mitsantisuk, C., Katsura, S. and Ohishi, K., “Force control of human-robot interaction using twin direct-drive motor system based on modal space design,” IEEE Trans. Ind. Electron. 57 (4), 1383–1392 (2010).CrossRefGoogle Scholar
24.Green, A. and Eklundh, K. S., “Designing for learnability in human-robot communication,” IEEE Trans. Ind. Electron. 50 (4), 644–650 (2003).CrossRefGoogle Scholar
25.Morioka, K., Lee, J. H. and Hashimoto, H., “Human-follow mobile robot in a distributed intelligent sensor network,” IEEE Trans. Ind. Electron. 51 (1), 229–237 (2004)CrossRefGoogle Scholar
26.Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A. and Siegwart, R., “Robots meet humans-interaction in public space,” IEEE Trans. Ind. Electron. 52 (6), 1530–1546 (2005).CrossRefGoogle Scholar
27.Wang, H. and Kosuge, K., “Towards an Understanding of Dancers' Coupled Body Dynamics for Waltz,” IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA (2011) pp.~2008–2013.Google Scholar
28.Abeygunawardhana, P. K. and Murakami, T., “Vibration suppression control of two-wheel mobile manipulator using resonance-ratio-control-based null-space control,” IEEE Trans. Ind. Electron. 57 (12), 4137–4146 (2010).CrossRefGoogle Scholar
29.Noh, J. S., Lee, G. H. and Jung, S., “Position control of a mobile inverted pendulum system using radial basis function network,” Int. J. Control Autom. Syst. 8 (1), 157–161 (2010).CrossRefGoogle Scholar
30.Kim, S. S. and Jung, S., “Control experiment of a wheel-driven mobile inverted pendulum using neural network,” IEEE Trans. Control Syst. Technol. 16 (2), 297–303 (2008).Google Scholar
31.Teeyapan, K., Wang, J., Kunz, T. and Stilman, M., “Robot Limbo: Optimized planning and control for dynamically stable robots under vertical obstacles,” IEEE Conference on Robotics and Automations, Anchorage, USA (2010) pp. 4519–4524.Google Scholar
32.Ahn, J. K., Lee, S. J. and Jung, S., “Force Control Application to a Mobile Manipulator with Balancing Mechanism,” IECON, Glendale, USA (2010) pp. 1522–1527.Google Scholar
33.Song, D. H., Lee, W. K. and Jung, S., “Kinematic analysis and implementation of a motion-following task for a humanoid slave robot controlled by an exoskeleton master robot,” Int. J. Control Autom. Syst. 5 (6), 681–690 (2007).Google Scholar
34.Lee, H. J. and Jung, S., “Guidance control of a wheeled mobile robot with human interaction based on force control,” Int. J. Control Autom. Syst. 8 (2), 361–368 (2010).CrossRefGoogle Scholar
35.Lee, H. J. and Jung, S., “Control of a Mobile Inverted Pendulum Robot System,” ICCAS, Seoul, Korea (2008) pp. 217–222.Google Scholar
36.Jung, S. and Kim, S. S., “Hardware implementation of a real-time neural network controller with a DSP and an FPGA for nonlinear systems,” IEEE Trans. Ind. Electron. 54 (1), 265–271 (2007).CrossRefGoogle Scholar