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Smooth gait optimization of a fish robot using the genetic-hill climbing algorithm

Published online by Cambridge University Press:  14 June 2011

Tuong Quan Vo ,
Hyoung Seok Kim  and
Byung Ryong Lee
Tuong Quan Vo
Affiliation:
School of Mechanical and Automotive Engineering, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan 680-749, South Korea
Hyoung Seok Kim
Affiliation:
School of Mechanical and Automotive Engineering, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan 680-749, South Korea
Byung Ryong Lee*
Affiliation:
School of Mechanical and Automotive Engineering, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan 680-749, South Korea
*
*Corresponding Author: E-mail: [email protected]
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Summary

This paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.

Keywords

Fish robotDynamicOptimization, GAHCASmooth gaitInput torqueBig jump
Type
Articles
Information
Robotica , Volume 30 , Issue 2 , March 2012 , pp. 257 - 278
Copyright
Copyright © Cambridge University Press 2011

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References

1.Yeow, C. S. and Chien, C. C., “Adaptive control schemes for autonomous underwater vehicle,” Robotica 27, 119–129 (2009).Google Scholar
2.McFarland, D., Gilhespy, I. and Honary, E., “DIVEBOT: A diving robot with a whale-like buoyancy mechanism,” Robotica 21, 385–398 (2003).CrossRefGoogle Scholar
3.Lauder, G. V. and Drucker, E. G., “Morphology and experimental hydrodynamics of fish fin control surfaces,” IEEE J. Ocean. Eng. 29 (3), 556–571 (2004, Jul.).CrossRefGoogle Scholar
4.Lighthill, M. J., “Hydromechanics of aquatic animal propulsion,” Annu. Rev. Fluid Mech. 1, 413–446 (1969, Jan.).CrossRefGoogle Scholar
5.Yu, J. and Wang, L., “Parameter Optimization of Simplified Propulsive Model for Biomimetic Robot Fish,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 3306–3311.Google Scholar
6.Zhao, W., Yu, J., Fang, Y. and Wang, L., “Development of Multi-mode Biomimetic Robotic Fish Based on Central Pattern Generator,” Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 9–15, 2006) pp. 3891–3896.Google Scholar
7.Zhang, D. B., Hu, D. W., Sheng, L. C. and Xie, H. B., “A bionic neural network for fish robot locomotion,” Sci. Direct J. Bionic Eng. 3, 187–194 (2006).CrossRefGoogle Scholar
8.Liu, J., Dukes, I., Knight, R. and Hu, H., “Development of Fish-Like Swimming Behaviours for An Autonomous Robotic Fish,” Proceedings of Control 2004, University of Bath, Bath, UK (Sep. 6–9, 2004) pp. 1049–1054.Google Scholar
9.Yu, J., Wang, S. and Tan, M., “A simplified propulsive model of bio-mimetic robot fish and its realization,” Robotica 23, 101–107 (2005).CrossRefGoogle Scholar
10.Lighthill, M. J., “Note on the swimming of slender fish,” J. fluid Mech. 9, 305–317 (1960).CrossRefGoogle Scholar
11.Seo, K., Murray, R. and Lee, J. S., “Exploring Optimal Gaits For Planar Carangiform Robot Fish Locomotion,” Proceedings of the 16th IFAC World Congress, Prague, Czech (Jul. 2005).Google Scholar
12.Guo, J., “A way-point tracking controller for a biomimetic autonomous underwater vehicle,” Ocean Eng. 33, 2369–2380 (2006).CrossRefGoogle Scholar
13.Liu, J. and Hu, H., “Building a Simulation Environment for Optimising Control Parameters of an Autonomous Robotic Fish,” Proceeding of the 9th Chinese Automation & Computing Society Conference, Luton, UK (Sep. 20, 2003).Google Scholar
14.Borazjani, I. and Sotiroulos, F., “Numerical investigation of the hydrodynamics of carangiform simming in the transitional and inertial flow regimes,” J. Exp. Biol. 211, 1541–1558 (2008).CrossRefGoogle ScholarPubMed
15.Low, K. H., Zhou, C. and Zhong, Yu, “Gait planning for steady swimming control of biomimetic fish robots,” Adv. Robot. 23, 805–829 (2009).CrossRefGoogle Scholar
16.Mason, R. and Burdick, J. W., “Experiment in Carangiform Robotic Fish Locomotion,” Proceedings of the 2000 IEEE International Conference on Robotics & Automation, San Francisco, CA (Apr. 2000).Google Scholar
17.Nakashima, M., Ohgishi, N. and Ono, K., “A study on the propulsive mechanism of a double jointed fish robot utilizing self-excitation control,” JSME Int. J. C 46 (3), 982–990 (2003).CrossRefGoogle Scholar
18.Reeves, C. R., Rowe, J. E., Genetic Algorithms – Principles And Perspectives, A Guide to GA Theory (Kluwer Academic, Massachusetts, 2003).Google Scholar
19.Haupt, R. L., Haupt, S. E., Practical Genetic Algorithms, 2nd ed. (John Willey, San Francisco, CA, 2004).Google Scholar
20.Moore, A. W., Iterative Improvement Search Hill Climbing, Simulated Annealing, WALKSAT, and Genetic Algorithms (School of Computer Science Carnegie Mellon University, Pittsburgh, PA, 2009). Available at www.cs.cmu.edu/~awmGoogle Scholar
21.Hagiwara, M., “Pseudo Hill Climbing Genetic Algorithm (PHGA) for Function Optimization,” Proceedings of the 1993 International Joint Conference on Neural Networks, Nagoya Congress Center, Japan (Oct. 25–29, 1993).Google Scholar
22.Houck, C. R., Joines, J. A. and Kay, M. G., “A Genetic Algorithm for Function Optimization: A Matlab Implementation,” Technical Report NCSU-IE-TR-95-09 (North Carolina State University, Raleigh, NC, 1995).Google Scholar