Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-18T20:52:09.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Design, analysis, and stiffness optimization of a three degree of freedom parallel manipulator

Published online by Cambridge University Press:  05 May 2009

Zhen Gao ,
Dan Zhang ,
Xiaolin Hu  and
Yunjian Ge
Zhen Gao
Affiliation:
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui Province 230031, P. R. China Department of Automation, University of Science and Technology of China, Hefei, Anhui Province 230027, P. R. China
Dan Zhang*
Affiliation:
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, CanadaL1H 7K4
Xiaolin Hu
Affiliation:
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, CanadaL1H 7K4
Yunjian Ge
Affiliation:
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui Province 230031, P. R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper proposed a novel three degree of freedom (DOF) parallel manipulator—two translations and one rotation. The mobility study and inverse kinematic analysis are conducted, and a CAD model is presented showing the design features. The optimization techniques based on artificial intelligence approaches are investigated to improve the system stiffness of the proposed 3-DOF parallel manipulator. Genetic algorithms and artificial neural networks are implemented as the intelligent optimization methods for the stiffness synthesis. The mean value and the standard deviation of the global stiffness distribution are proposed as the design indices. Both the single objective and multi-objective optimization issues are addressed. The effectiveness of this methodology is validated with Matlab.

Keywords

Parallel manipulatorStiffness optimizationArtificial intelligence approachesdegree of freedomParallelogram structure
Type
Article
Information
Robotica , Volume 28 , Issue 3 , May 2010 , pp. 349 - 357
Copyright
Copyright © Cambridge University Press 2009

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.Zhang, Dan and Gosselin, Clément M., “Parallel kinematic machine design with kinetostatic model,” Robotica 4 (7), 429–438 (2002).CrossRefGoogle Scholar
2.Carretero, J. A., Podhorodeski, R. P., Nahon, M. N. and Gosselin, C. M., “Kinematic analysis and optimization of a new three degree-of-freedom spatial parallel manipulator,” ASME J. Mech. Des. 122, 17–24 (2000).CrossRefGoogle Scholar
3.Dunlop, G. R. and Jones, T. P, “Position analysis of a two DOF parallel mechanism-Canterbury tracker,” Mech. Mach. Theory 34, 599–614 (1999).CrossRefGoogle Scholar
4.Zhang, Dan and Wang, L., “PKM capabilities and applications exploration in a collaborative virtual environment,” Robot. Comput.-Integr. Manuf. 22, 384–395 (2006).CrossRefGoogle Scholar
5.Soons, J. A., “On the Geometric and Thermal Errors of a Hexapod Machine Tools, Parallel Kinematic Machines: Theoretical Aspects and Industrial Requirements,” In: Advanced Manufacturing Series (Boer, C. R., Molinari-Tosatti, L. and Smith, K. S., eds.), (Springer-Verlag, 1999) pp. 151–170.CrossRefGoogle Scholar
6.Zhang, Dan and Gosselin, Clément M., “Kinetostatic modeling of parallel mechanisms with a passive constraining leg and revolute actuators,” Mech. Mach. Theory 37, 599–617 (2002).CrossRefGoogle Scholar
7.Bi, Z. M., Lang, S. Y. T., Zhang, Dan, Orban, P. E. and Verner, M., “An integrated design toolbox for tripod-based parallel kinematic machines,” J. Mech. Des.: Trans. ASME, 129, 799–807 (2007).CrossRefGoogle Scholar
8.Zhang, Dan and Wang, L., “Conceptual development of an enhanced tripod mechanism for machine tool,” Robot. Comput.-Integr. Manuf. 21, 318–327 (2002).CrossRefGoogle Scholar
9.Staicu, Stefan, Zhang, Dan and Rugescu, Radu Dan, “Dynamic modelling of a 3-DOF parallel manipulator using iterative matrix relations,” Robotica 24 (01), 125–130 (2006).CrossRefGoogle Scholar
10.Liu, Xin-Jun, Wang, Jinsong and Pritschow, Gunter, “A new family of spatial 3-DoF fully-parallel manipulators with high rotational capability,” Mech. Mach. Theory 40, 475–494 (2005).CrossRefGoogle Scholar
11.Zhang, Dan, Wang, L. and Esmailzadeh, E., “PKM capabilities and applications exploration in a collaborative virtual environment”, Robot. Comput.-Integr. Manuf. 22, 384–395 (2006).CrossRefGoogle Scholar
12.Bergamaschi, Paulo Roberto, Nogueira, Antonio Carlos and Saramago, Sezimaria de Fatima Pereira, “Design and optimization of 3R manipulators using the workspace features,” Appl. Math. Comput., 172, 439–463 (2006).Google Scholar
13.Kucuk, S. and Bingul, Z., “Robot Workspace Optimization Based on a Novel Local and Global Performance Indices,” IEEE ISIE 2005, Dubrovnik, Croatia (June 20–23, 2005) pp. 1593–1598.Google Scholar
14.Yu, Alexander, Bonev, Ilian A. and Zsombor-Murray, Paul, “Geometric approach to the accuracy analysis of a class of 3-DOF planar parallel robots,” Mech. Mach. Theory (2007) Available online.CrossRefGoogle Scholar
15.Zhao, Jing-Shan, Zhang, Sheng-Lan, Dong, Jing-Xin, Feng, Zhi-Jing and Zhou, Kai, “Optimizing the kinematic chains for a spatial parallel manipulator via searching the desired dexterous workspace,” Robot. Comput.-Integr. Manuf. 23, 38–46 (2007).CrossRefGoogle Scholar
16.Stock, Michael and Miller, Karol, “Optimal kinematic design of spatial parallel manipulators: Application to linear delta robot,” J. Mech. Des.: Trans. ASME. 125, 292–301 (June 2003).CrossRefGoogle Scholar
17.Rout, B. K. and Mittal, R. K., “Parametric design optimization of 2-DOF R–R planar manipulator: A design of experiment approach,” Robot. Comput.-Integr. Manuf. 24, 239–248 (2008).CrossRefGoogle Scholar
18.Ceccarelli, Marco and Lanni, Chiara, “A multi-objective optimum design of general 3R manipulators for prescribed workspace limits,” Mech. Mach. Theory 39, 119–132 (2004).CrossRefGoogle Scholar
19.Holland, J. H., Adaptation in Natural and Artificial Systems (The University of Michigan Press, Ann Arbor, MI, 1975).Google Scholar
20.Zhang, Dan, Kinetostatic Analysis and Optimization of Parallel and Hybrid Architectures for Machine Tools Ph.D. Thesis (Quebec, QC, Canada: Laval University, 2000).Google Scholar
21.Gosselin, C. M. and Guillot, M., “The synthesis of manipulators with prescribed workspace,” J. Mech. Des.: Trans. ASME 113, 451–455 (1991).CrossRefGoogle Scholar
22.Garcıa-Pedrajas, N., Hervas-Martınez, C. and Munoz-Perez, J., “Multi-objective cooperative coevolution of artificial neural networks,” Neural Netw. 15, 1259–1278 (2002).CrossRefGoogle ScholarPubMed
23.Gupta, Shantanu, Tiwari, Rajiv and Nair, Shivashankar B., “Multi-objective design optimization of rolling bearings using genetic algorithms,”. Mech. Mach. Theory 42, 1418–1443 (2007).CrossRefGoogle Scholar