Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-29T11:08:31.289Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimal design of manipulator parameter using evolutionary optimization techniques

Published online by Cambridge University Press:  07 July 2009

B. K. Rout  and
R. K. Mittal
B. K. Rout*
Affiliation:
Mechanical Engineering Group, Birla Institute of Technology and Science, Pilani – 333031, Rajasthan, India
R. K. Mittal
Affiliation:
Mechanical Engineering Group, Birla Institute of Technology and Science, Pilani – 333031, Rajasthan, India
*
*Corresponding author. E-mail: bijoykumarraut@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Summary

A robot must have high positioning accuracy and repeatability for precise applications. However, variations in performance are observed due to the effect of uncertainty in design and process parameters. So far, there has been no attempt to optimize the design parameters of manipulator by which performance variations will be minimum. A modification in differential evolution optimization technique is proposed to incorporate the effect of noises in the optimization process and obtain the optimal design of manipulator, which is insensitive to noises. This approach has been illustrated by selecting optimal parameter of 2-DOF RR planar manipulator and 4-DOF SCARA manipulator. The performance of proposed approach has been compared with genetic algorithm with similar modifications. It is observed that the optimal results are obtained with lesser computations in case of differential evolution technique. This approach is a viable alternative for costly prototype testing, where only kinematic and dynamic models of manipulator are dealt with.

Keywords

Manipulator performance variationsNoise factorsPositional errorOrientation errorMean positional errorMean orientation errorOrthogonal arrayEvolutionary optimization technique
Type
Article
Information
Robotica , Volume 28 , Issue 3 , May 2010 , pp. 381 - 395
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.Khatib, O. and Burdick, J., “Optimization of dynamics in manipulator design: The operational space formulation,” Int. J. Rob. Automat. 2, 90–98(1987).Google Scholar
2.Manoochehri, S. and Seireg, A. A., “A computer based methodology for the form synthesis and optimal design of robot manipulators,” ASME J. Mech. Des. 112, 501–507(1990).CrossRefGoogle Scholar
3.Shiller, Z. and Sundar, S., “Design of multi-degree-of-freedom mechanisms for optimal dynamic performance,” ASME J. Mech. Des. 115, 199–206(1993).CrossRefGoogle Scholar
4.Khatib, O. and Bowling, A., “Optimization of the Inertial and Acceleration Characteristics of Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation, MN, Vol. 4 (1996) pp. 2883–2889.CrossRefGoogle Scholar
5.Chocron, O. and Bidaud, P., “Genetic Design of 3D Modular Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation, Albuquerque, NM, Vol. 1 (1997) pp. 223–228.CrossRefGoogle Scholar
6.Stocco, L., Salcudean, S. E. and Sassani, F., “Fast constrained global minimax optimization of robot parameters,” Robotica 16, 595–605(1998).CrossRefGoogle Scholar
7.Carretero, J. A., Podhorodeski, R. P., Nahon, M. A. and Gosselin, C. M., “Kinematic analysis and optimization of a new three degree-of-freedom spatial parallel manipulator,” J. Mech. Des. 122, 17–24(1987).CrossRefGoogle Scholar
8.Rao, S. S. and Bhatti, P. K., “Probabilistic approach to manipulator kinematics and dynamics,” Relb. Eng. Syst. Safety 72, 47–58(2001).CrossRefGoogle Scholar
9.Shiakolas, P. S., Koladiya, D. and Kebrle, J., “Optimum robot design based on task specifications using evolutionary techniques kinematic, dynamic, and structural constraints,” Inverse Prob. Eng. 10, 359–375(2002).CrossRefGoogle Scholar
10.Tian, L. and Collins, C., “Optimal placement of a two-link planar manipulator using a genetic algorithm,” Robotica 23, 169–176(2005).CrossRefGoogle Scholar
11.Kim, J. Y., “Task based kinematic design of a two DOF manipulator with a parallelogram five-bar link mechanism,” Mechatronics 16, 323–329(2006).CrossRefGoogle Scholar
12.Tabandeh, S., Clark, C. and Melek, W., “A Genetic Algorithm Approach to Solve for Multiple Solutions of Inverse Kinematics Using Adaptive Niching and Clustering,” Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, British Columbia, Canada (2006) pp. 1815–1822.Google Scholar
13.Huang, P., Yan, J., Xu, Y., Xu, W. and Liang, B., “Genetic Algorithms-Based Minimum Torque Path Planning for Space Manipulator,” Proceedings of IEEE International Conference on Robotics and Automation, Vol. 1 (2006) pp. 3575–3579.Google Scholar
14.He, G., Gao, H., Zhang, G. and Wu, L., “Using Adaptive Genetic Algorithm to the Placement of Serial Robot Manipulator,” Proceedings of IEEE International Conference on Engineering of Intelligent Systems (2006) pp. 1–6. doi:10.1109/ICEIS.2006.1703222.CrossRefGoogle Scholar
15.Dolinsky, J. U., Jenkinson, I. D. and Colquhoun, G. J., “Application of genetic programming to the calibration of industrial robots,” Comp. Indus. 58, 255–264(2007).CrossRefGoogle Scholar
16.Coello Coello, C. A., Christiansen, A. D. and Aguirre, A. H., “Using a new GA-based multi-objective optimization technique for the design of robot arms,” Robotica 16, 401–414(1998).CrossRefGoogle Scholar
17.Han, J., Chung, W. E., Youm, Y. and Kim, S. H., “Task Based Design of Modular Robot Manipulator using Efficient Genetic Algorithm,” Proceedings of the 1997 IEEE International Conference on Robotics and Automation, Albuquerque, NM (April 1997).Google Scholar
18.Rout, B. K., Sharma, N. N. and Mittal, R. K., “Optimal Design of Manipulator Parameters Using Evolutionary Optimization Techniques,” Proceedings of 3rd International Conference on Autonomous Robots and Agents, Palmerston North, Massey University New Zealand (Dec 12–14, 2006) pp. 659–664.Google Scholar
19.Rout, B. K. and Mittal, R. K., “Screening of factors influencing the performance of manipulator using combined array design of experiment approach,” Rob. Comp. Integr. Manuf. 25 (3), 651–666(2008). doi:10.1016/j.rcim.2008.05.004.CrossRefGoogle Scholar
20.Paredis, C. J. J. and Khosla, P. K., “Synthesis Methodology for Task Based Reconfiguration of Modular Manipulator Systems,” Proceedings of the 6th International Symposium on Robotics Research, Hidden Valley, PA (Oct. 2–5, 1993).Google Scholar
21.Paredis, C. J. J. and Khosla, P. K., “Kinematic design of serial link manipulators from task specifications,” Int. J. Rob. Res. 12 (3), 274–87(1993).CrossRefGoogle Scholar
22.Paredis, C. J. J., Brown, H. B. and Khosla, P. K., “A Rapidly Deployable Manipulator System,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN (April 1996) pp. 1434–1439.CrossRefGoogle Scholar
23.Paredis, C. J. J., An Agent-Based Approach to the Design of Rapidly Deployable Fault Tolerant Manipulators, Ph.D. Thesis (Department of Electrical and Computer Engineering, Carnegie Mellon University, August 1996).Google Scholar
24.Yang, G. and Chen, I.-M., “Task-based optimization of modular robot configurations-minimized degree-of-freedom approach,” Mech. Mach. Theory 35 (4), 517–540(2000).CrossRefGoogle Scholar
25.Chen, I.-M., “Rapid response manufacturing through a rapidly reconfigurable robotic workcell,” Rob. Comp. Integr. Manuf. 17, 199–213(2001).CrossRefGoogle Scholar
26.Chen, I.-M., “Realization of a rapidly reconfigurable robotic workcell,” J. Jpn. Soc. Precision Eng. 66 (7), 1024–1030(2000).CrossRefGoogle Scholar
27.Storn, R., “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” J. Global Optim. 11, 341–359(1995).CrossRefGoogle Scholar
28.Storn, R., Differential Evolution Design of an IIR-Filter with Requirements for Magnitude and Group Delay (International Computer Science Institute, Berkeley, California, 1995).Google Scholar
29.Price, K. and Storn, R., “A simple evolution strategy for fast optimization,” Dr. Dobb's J. 264, 18–24(1997).Google Scholar
30.Price, K. and Storn, R., “Home page of differential evolution,” URL: http://www.ICSI.Berkeley.edu/_storn/code.html (2006).Google Scholar
31.Hacker, Kurt and Lewis, Kemper, “Robust Design Through the use of a Hybrid Genetic Algorithm,” Proceedings of DETC'02, ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada (Sep. 29–Oct. 2, 2002).Google Scholar
32.Bagchi, T. P., “Multi-objective robust design by genetic algorithms,” Mater. Manuf. Process. 18, 341–354(2003).CrossRefGoogle Scholar
33.Hoel, P. G., Port, S. C. and Stone, C. J., Introduction to Stochastic Processes (Houghton Mifflin Company, Boston, USA, 1986).Google Scholar
34.Park, S. H., Robust Design and Analysis for Quality Engineering (Chapman and Hall, London, 1998).Google Scholar
35.Box, G. E. P., Jenkins, G. M. and Gregory, C. R., Time series Analysis: Forecasting and Control, 3rd ed. (Pearson Education, Singapore, 1994).Google Scholar
36.Craig, J. J., Introduction to Robotics Mechanics and Control, 2nd ed. (Pearson Education, Singapore, 1989).Google Scholar
37.Mittal, R. K., Nagrath, I. J., Robotics and Control (Tata McGraw-Hill Publishing Company Limited, New Delhi, 2003).Google Scholar
38.Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control (John Wiley & Sons, Singapore, 1989).Google Scholar
39.Deb, K., Optimization for Engineering Design: Algorithms and Examples (Prentice Hall of India Private Limited, New Delhi, 2003).Google Scholar
40.Onwubolu, G. C. and Babu, B. V., New Optimization Techniques in Engineering (Springer, Heidelberg, Germany, 2004).CrossRefGoogle Scholar