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Application of statistical techniques in modeling and optimization of a snake robot

Published online by Cambridge University Press:  16 November 2012

Hadi Kalani ,
Alireza Akbarzadeh  and
Hossein Bahrami
Hadi Kalani
Affiliation:
Center of Excellence on Soft Computing and Intelligent Information Processing (SCIPP), Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Alireza Akbarzadeh*
Affiliation:
Center of Excellence on Soft Computing and Intelligent Information Processing (SCIPP), Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Hossein Bahrami
Affiliation:
Center of Excellence on Soft Computing and Intelligent Information Processing (SCIPP), Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
*
*Corresponding author. E-mail: Ali_akbarzadeh_t@yahoo.com
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Summary

This paper provides a general framework based on statistical design and Simulated Annealing (SA) optimization techniques for the development, analysis, and performance evaluation of forthcoming snake robot designs. A planar wheeled snake robot is considered, and the effect of its key design parameters on its performance while moving in serpentine locomotion is investigated. The goal is to minimize energy consumption and maximize distance traveled. Key kinematic and dynamic parameters as well as their corresponding range of values are identified. Derived dynamic and kinematic equations of n-link snake robot are used to perform simulation. Experimental design methodology is used for design characterization. Data are collected as per full factorial design. For both energy consumption and distance traveled, logarithmic, linear, and curvilinear regression models are generated and the best models are selected. Using analysis of variance, ANOVA, effects of parameters on performance of robots are determined. Next, using SA, optimum parameter levels of robots with different number of links to minimize energy consumption and maximize distance traveled are determined. Both single and multi-criteria objectives are considered. Webots and Matlab SimMechanics software are used to validate theoretical results. For the mathematical model and the selected range of values considered, results indicate that the proposed approach is quite effective and efficient in optimization of robot performance. This research extends the present knowledge in this field by identifying additional parameters having significant effect on snake robot performance.

Keywords

Snake robotDOESerpentine gaitDynamicEnergyDistanceRegressionOptimizationSimulated annealing
Type
Articles
Information
Robotica , Volume 31 , Issue 4 , July 2013 , pp. 623 - 641
Copyright
Copyright © Cambridge University Press 2012 

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References

1.Hirose, S., Biologically Inspired Robots: Snake-Like Locomotors and Manipulators (Oxford University Press, Oxford, UK, 1993).Google Scholar
2.Dowling, K., “Limbless Locomotion: Learning to Crawl with a Snake Robot,” Ph.D. Thesis (Pittsburgh, PA: Robotics Institute, Carnegie Mellon University, 1997).Google Scholar
3.Ostrowski, J. and Burdick, J., “Gait Kinematics for a Serpentine Robot,” Proceedings of the1996 IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota (Apr. 1996).Google Scholar
4.Shugen, M.A., “Analysis of Snake Movement Forms for Realization of Snake-Like Robots,” Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan (May 1999).Google Scholar
5.Shugen, M., Tadokoro, N., Inoue, K. E. and Liz, B., “Influence of Inclining Angle of a Slope to Optimal Locomotion Curves of a Snake-Like Robot,” Proceedings of the 2003 IEEE Changsha International Conference on Robotics, Intelligent Systems and Signal Processing, China (Oct. 2003).Google Scholar
6.Saito, M., Fukaya, M. and Iwasaki, T., “Serpentine locomotion with robotic snakes,” IEEE Control Syst. Mag. 22, 64–81 (2002).Google Scholar
7.Hasanzadeh, S. and Tootoonchi, A. Akbarzadeh, “Ground adaptive and optimized locomotion of snake robot moving with a novel gait,” Auton. Robot. 28, 457–470 (2010).CrossRefGoogle Scholar
8.Hasanzadeh, Sh. and Akbarzadeh, A., “Development of a new spinning gait for a planar snake robot using central pattern generators,” Intel. Serv. Robot. (Submitted).Google Scholar
9.Akbarzadeh, A. and Kalani, H., “Design and modeling of a snake robot based on worm-like locomotion,” Adv. Robot. 26, 537–560 (2012).CrossRefGoogle Scholar
10.Kalani, H., Akbarzadeh, A. and Safehian, J., “Traveling Wave Locomotion of Snake Robot along Symmetrical and Unsymmetrical Body Shapes,” Proceedings for the Joint Conference of ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics) (ISR-Robotik) Munich, Germany (Jun. 7–9, 2010).Google Scholar
11.Gomez, J. G., “Modular Robotics and Locomotion: Application to Limbless Robots,” Ph.D. Thesis (Madrid, 2008).Google Scholar
12.Tavakkoli-Moghaddam, R., Safaei B, N. and Gholipour, Y., “A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length,” Appl. Math. Comput. 176, 445–454 (2006).Google Scholar
13.Hopkins, J. K., Spranklin, B. W. and Gupta, S. K., “A survey of snake-inspired robot designs,” Bionispiration and Biomimetics 4 (2), 021001 (2009).CrossRefGoogle ScholarPubMed
14.Nilsson, M., “Serpentine Locomotion on Surfaces with Uniform Friction,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendal, Japan (Sep. 28–Oct. 2, 2004).Google Scholar
15.Liljebäck, P., Pettersen, K. Y., Stavdahl, Ø. and Gravdahl, J. T, “Fundamental Properties of Snake Robot Locomotion,” Proceedings of the IEEE RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (Oct. 18–22, 2010).Google Scholar
16.Nakhaee Nejad, M., “On the Design and Motion Analysis of a Snake-Like Manipulator,” M.Sc. Thesis (Tehran, Iran: Applied & Solid Mechanics Division, School of Mechanical Engineering, Sharif University of Technology).Google Scholar
17.Nakhaee Nejad, M., Meghdari, A. and Naderi, D., “Modeling and Dynamics Analysis of Snake-Like Robot Manipulators,” In: Proceedings of the 11th ISME Annual (International) Mechanical Engineering Conference, Iran vol. 4 (May 2003) pp. 2026–2033.Google Scholar
18.Nakhaee Nejad, M., Meghdari, A. and Naderi, D., “Dynamic Motion Analysis of a Snake-Like Robot on a Slopped Surface using Nueral Network,” Proceedings of the 12th ISME Annual Mechanical Engineering Conference, Iran (Apr. 2004).Google Scholar
19.Andreas Transeth, A., Ytterstad Pettersen, K. and Liljebäck, P., “A survey on snake robot modeling and locomotion,” Robotica 27, 999–1015 (2009).CrossRefGoogle Scholar
20.Chirikjian, G. and Burdick, J., “The kinematics of hyper-redundant robot locomotion,” IEEE Trans. Robotics Automat. 11, 781–793 (1995).CrossRefGoogle Scholar
21.Hu, D. L., Nirody, J., Scott, T. and Shelley, M. J., “The mechanics of slithering locomotion,” PNAS (2009, Jun. 8) doi:10.1073/pnas.0812533106.CrossRefGoogle ScholarPubMed
22.Tesch, M., Schneider, J. and Choset, H., “Using Response Surfaces and Expected Improvement to Optimize Snake Robot Gait Parameters,” IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California (Sep. 2011).Google Scholar
23.Rout, B. K. and Mittal, R. K., Parametric design optimization of 2-DOF R–R planar manipulator – a design of experiment approach,” Robot. Comput. Integ. Manuf. 24, 239–248 (2008).CrossRefGoogle Scholar
24.Rout, B. K. and Mittal, R. K., “Screening of factors influencing the performance of manipulator using combined array design of experiment approach,” Robot. Comput. Integr. Manuf. 25, 651–666 (2009).CrossRefGoogle Scholar
25.Rout, B. K. and Mittal, R. K., “Tolerance design of robot parameters using Taguchi method,” Mech. Syst. Signal Process. 20, 1832–1852 (2006).CrossRefGoogle Scholar
26.Wu, C. M., Black, J. T. and Jiang, B. C., “Using Taguchi methods to determine/optimize robot process capability for path following,” Robot. Comput. Integr. Manuf. 8, 9–25 (1991).CrossRefGoogle Scholar
27.Yang, K. and EI-Haik, B., Design for Six Sigma (McGraw-Hill, New York City, New York, 2000, ISBN: 0071547673).Google Scholar
28.Casella, G., Statistical Design, 1st ed. (Springer Texts in Statistics) (Springer, New York, 2008).CrossRefGoogle Scholar
29.Kirkpatrick, G. J. and Vecchi, D. D., “Optimization by simulated annealing,” Science 220, 671–680 (1983).CrossRefGoogle ScholarPubMed
30.Minitab Inc, “Minitab software, revision 16,” Availablev at: http://www.minitab.com/en-US/default.aspx (2011).Google Scholar
31.Cyberbotics, Webots Reference Manual, release 6.4.1, available at: www.cyberbotics.com (Aug. 31, 2011).Google Scholar
32.Metropolis, N., Rosenbluth, A. W., Rosenbluth, M., Teller, A. H., and Teller, E., “Equation of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).CrossRefGoogle Scholar