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Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: numerical simulation and experimental verification

Published online by Cambridge University Press:  07 December 2010

Akira Abe
Akira Abe*
Affiliation:
Department of Information System Engineering, Asahikawa National College of Technology, 2-2-1-6 Syunkodai, Asahikawa, Hokkaido 071-8142, Japan
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a novel trajectory planning method for a flexible Cartesian robot manipulator in a point-to-point motion. In order to obtain an exact mathematical model, the parameters of the equation of motion are determined from an identification experiment. An artificial neural network is employed to generate the desired base position, and then, a particle swarm optimization technique is used as the learning algorithm, in which the sum of the displacements of the manipulator is chosen as the objective function. We show that the residual vibrations of the manipulator can be suppressed by minimizing the displacement of the manipulator. The effectiveness and validity of the proposed method are demonstrated by comparing the simulation and experimental results.

Keywords

Trajectory planningFlexible Cartesian robot manipulatorVibration controlArtificial neural networkParticle swarm optimization
Type
Articles
Information
Robotica , Volume 29 , Issue 5 , September 2011 , pp. 797 - 804
Copyright
Copyright © Cambridge University Press 2010

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References

1.Dwivedy, S. K. and Eberhard, P., “Dynamic analysis of flexible manipulators, A literature review,” Mech. Mach. Theory 41 (7), 749–777 (2006).CrossRefGoogle Scholar
2.Benosman, M. and Vey, L. G., “Control of flexible manipulators: A survey,” Robotica 22 (5), 535–545 (2004).CrossRefGoogle Scholar
3.Singer, N. C. and Seering, W. P., “Preshaping command inputs to reduce system vibration,” Trans. ASME J. Dyn. Syst. 112 (1), 76–82 (1990).CrossRefGoogle Scholar
4.Magee, D. P. and Book, W. J., “Eliminating Multiple Modes of Vibration in a Flexible Manipulator,” Proceedings of IEEE International Conference on Robotics and Automation, Atlanta, GA, USA (May 2–6, 1993) pp. 474–479.Google Scholar
5.Singhose, W. E. and Singer, N. C., “Effects of input shaping on two-dimensional trajectory following,” IEEE Trans. Robot. Autom. 12 (6), 881–887 (1996).CrossRefGoogle Scholar
6.Cho, J. K. and Park, Y. S., “Experimental evaluation of time-varying impulse shaping with a two-link flexible manipulator,” Robotica 14 (3), 339–345 (1996).CrossRefGoogle Scholar
7.Mimmi, G. and Pennacchi, P., “Pre-shaping motion input for a rotating flexible link,” Int. J. Solids Struct. 38 (10/13), 2009–2023 (2001).CrossRefGoogle Scholar
8.Lee, K. S. and Park, Y. S., “Residual vibration reduction for a flexible structure using a modified input shaping technique,” Robotica 20 (5), 553–561 (2002).CrossRefGoogle Scholar
9.Mohamed, Z. and Tokhi, M. O., “Command shaping techniques for vibration control of a flexible robot manipulator,” Mechatronics 39 (11), 1039–1047 (2003).Google Scholar
10.Alam, M. S. and Tokhi, M. O., “Designing feedforward command shapers with multi-objective genetic optimisation for vibration control of a single-link flexible manipulator,” Eng. Appl. Artif. Intel. 21 (2), 229–246 (2008).CrossRefGoogle Scholar
11.Park, K. J. and Park, Y. S., “Fourier-based optimal design of a flexible manipulator path to reduce residual vibration of the endpoint,” Robotica 11 (3), 263–272 (1993).CrossRefGoogle Scholar
12.Park, K. J., “Path design of redundant flexible robot manipulators to reduce residual vibration in the presence of obstacles,” Robotica 21 (3), 335–340 (2003).CrossRefGoogle Scholar
13.Park, K. J., “Flexible robot manipulator path design to reduce the endpoint residual vibration under torque constraints,” J. Sound Vib. 275 (3/5), 1051–1068 (2004).CrossRefGoogle Scholar
14.Mohri, A., Sarkar, P. K. and Yamamoto, M., “An Efficient Motion Planning of Flexible Manipulator Along Specified Path,” Proceedings of IEEE International Conference on Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 1104–1109.Google Scholar
15.Kojima, H. and Kibe, T., “Optimal Trajectory Planning of a Two-Link Flexible Robot Arm Based on Genetic Algorithm for Residual Vibration Reduction,” Proceeding of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii, USA (Oct. 29–Nov. 3, 2001) pp. 2276–2281.Google Scholar
16.Benosman, M., Vey, G. L., Lanari, L. and Luca, A. D., “Rest-to-rest motion for planar multi-link flexible manipulator through backward recursion,” Trans. ASME J. Dyn. Syst. 126 (1), 115–123 (2004).CrossRefGoogle Scholar
17.Abe, A., “Trajectory planning for residual vibration suppression of a two-link rigid-flexible manipulator considering large deformation,” Mech. Mach. Theory 44 (9), 1627–1639 (2009).CrossRefGoogle Scholar
18.Kennedy, J. and Eberhart, R. C., “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, Australia (Nov. 27–Dec. 1, 1995) pp. 1942–1948.Google Scholar
19.Clerc, M. and Kennedy, J., “The particle swarm – Explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evolut. Comput. 6 (1), 58–73 (2002).CrossRefGoogle Scholar
20.Chatterjee, A., Pulasinghe, K., Watanabe, K. and Izumi, K., “A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems,” IEEE Trans. Ind. Electron. 52 (6), 1478–1489 (2005).CrossRefGoogle Scholar
21.Feng, H. M., “Self-generation RBFNs using evolutional PSO learning,” Neurocomputing 70 (1–3), 241–251 (2006).CrossRefGoogle Scholar
22.Chau, K. W., “Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River,” J. Hydrol. 329 (3–4), 363–367 (2006).CrossRefGoogle Scholar
23.Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A. and Tourassi, G. D., “Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance,” Neural Netw. 21 (2–3), 427–436 (2008).CrossRefGoogle ScholarPubMed
24.Geethanjali, M., Mary, S., Slochanal, Raja and Bhavani, R., “PSO trained ANN-based differential protection scheme for power transformers,” Neurocomputing 71 (4–6), 904–918 (2008).CrossRefGoogle Scholar