Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-21T02:14:10.176Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning to swim: a dynamical systems approach to mimicking fish swimming with CPG

Published online by Cambridge University Press:  18 July 2012

Tianmiao Wang ,
Yonghui Hu  and
Jianhong Liang
Tianmiao Wang
Affiliation:
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. China
Yonghui Hu*
Affiliation:
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, P. R. China
Jianhong Liang
Affiliation:
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. China
*
*Corresponding author. E-mail: huyhui@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

Central Pattern Generators (CPGs) can generate robust, smooth and coordinated oscillatory signals for locomotion control of robots with multiple degrees of freedom, but the tuning of CPG parameters for a desired locomotor pattern constitutes a tremendously difficult task. This paper addresses this problem for the generation of fish-like swimming gaits with an adaptive CPG network on a multi-joint robotic fish. Our approach converts the related CPG parameters into dynamical systems that evolve as part of the CPG network dynamics. To reproduce the bodily motion of swimming fish, we use the joint angles calculated with the trajectory approximation method as teaching signals for the CPG network, which are modeled as a chain of coupled Hopf oscillators. A novel coupling scheme is proposed to eliminate the influence of afferent signals on the amplitude of the oscillator. The learning rules of intrinsic frequency, coupling weight and amplitude are formulated with phase space representation of the oscillators. The frequency, amplitudes and phase relations of the teaching signals can be encoded by the CPG network with adaptation mechanisms. Since the Hopf oscillator exhibits limit cycle behavior, the learned locomotor pattern is stable against perturbations. Moreover, due to nonlinear characteristics of the CPG model, modification of the target travelling body wave can be carried out in a smooth way. Numerical experiments are conducted to validate the effectiveness of the proposed learning rules.

Keywords

Biomimetic robotic fishCentral pattern generatorFish swimming kinematicsDynamical systemsLearning
Type
Articles
Information
Robotica , Volume 31 , Issue 3 , May 2013 , pp. 361 - 369
Copyright
Copyright © Cambridge University Press 2012

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.Sfakiotakis, M., Lane, D. M. and Davies, J. B. C., “Review of fish swimming modes for aquatic locomotion,” IEEE J. Ocean. Eng. 24, 237–252 (Apr. 1999).CrossRefGoogle Scholar
2.Bandyopadhyay, P. R., “Trends in biorobotic autonomous undersea vehicles,” IEEE J. Ocean. Eng. 30 (1), 109–139 (2005).CrossRefGoogle Scholar
3.Triantafyllou, M. S. and Triantafyllou, G. S., “An efficient swimming machine,” Sci. Amer. 272 (3), 64–70 (1995).CrossRefGoogle Scholar
4.Tan, G., Shen, G., Huang, S., Su, W. and Ke, Y., “Investigation of flow mechanism of a robotic fish swimming by using flow visualization synchronized with hydrodynamic force measurement,” Exp. Fluids 43 (5), 811–821 (2007).CrossRefGoogle Scholar
5.Chen, Z., Shatara, S. and Tan, X., “Modeling of biomimetic robotic fish propelled by an ionic polymerCmetal composite caudal fin,” IEEE/ASME Trans. Mechatron. 15 (3), 448–459 (2010).CrossRefGoogle Scholar
6.Morgansen, K. A., Triplett, B. I. and Klein, D. J., “Geometric methods for modeling and control of free-swimming fin-actuated underwater vehicles,” IEEE Trans. Robot. 23 (6), 1184–1199 (2007).CrossRefGoogle Scholar
7.Hu, Y., Zhao, W., Xie, G. and Wang, L., “Development and target following of vision-based autonomous robotic fish,” Robotica 27, 1075–1089 (2009).CrossRefGoogle Scholar
8.Anderson, J. M. and Kerrebrock, P. A., “The Vorticity Control Unmanned Undersea Vehicle (VCUUV) – An Autonomous Vehicle Employing Fish Swimming Propulsion and Maneuvering,” In: Proceedings of the 10th International Symposia on Unmanned Untethered Submersible Technology, Durham, NH (1997) pp. 189–195.Google Scholar
9.Liang, J., Wang, T. and Wen, L., “Development of a two-joint robotic fish for real-world exploration,” J. Field Robot. 28 (1), 70–79 (2011).CrossRefGoogle Scholar
10.Terada, Y. and Yamamoto, I., “An animatronic system including lifelike robotic fish,” Proc. IEEE 92 (11), 1814–1820 (2004).CrossRefGoogle Scholar
11.Hu, H., Liu, L., Dukes, I. and Francis, G., “Design of 3D Swim Patterns for Autonomous Robotic Fish,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2006) pp. 2406–2411.Google Scholar
12.Shao, J. and Wang, L., “Platform for Cooperation of Multiple Robotic Fish – Robofish Water Polo,” In: Proceedings of the 46th IEEE Conference on Decision and Control (2007) pp. 1423–1428.Google Scholar
13.Yu, J., Tan, M., Wang, S. and Chen, E., “Development of a biomimetic robotic fish and its control algorithm,” IEEE Trans. Syst. Man Cybern. Part B Cybern. 34 (4), 1798–1810 (2004).CrossRefGoogle ScholarPubMed
14.Liu, J. and Hu, H., “Biological inspiration: From carangiform fish to multi-joint robotic fish,” J. Bionic Eng. 7 (1), 35–48 (2010).CrossRefGoogle Scholar
15.Low, K. and Willy, A., “Biomimetic motion planning of an undulating robotic fish fin,” J. Vib. Control 12 (12), 1337–1359 (2006).CrossRefGoogle Scholar
16.Hirata, K., “Development of Experimental Fish Robot,” In: Proceedings of the 6th International Symposia on Marine Engineering (2007) pp. 711–714.Google Scholar
17.Grillner, S., “Neurobiological bases of rhythmic motor acts in vertebrates,” Science 228 (4696), 143–149 (1985).CrossRefGoogle ScholarPubMed
18.Ijspeert, A. J., “Central pattern generators for locomotion control in animals and robots: A review,” Neural Netw. 21 (4), 642–653 (2008).CrossRefGoogle ScholarPubMed
19.Crespi, A., Lachat, D., Pasquier, A. and Ijspeert, A. J., “Controlling swimming and crawling in a fish robot using a central pattern generator,” Auton. Robot. 25 (1–2), 3–13 (2008).CrossRefGoogle Scholar
20.Zhang, D., Hu, D., Shen, L. and Xie, H., “Design of an artificial bionic neural network to control fish-robot's locomotion,” Neurocomputing 71 (4–6), 648–654 (2008).CrossRefGoogle Scholar
21.Zhao, W., Hu, Y., Zhang, L. and Wang, L., “Design and CPG-based control of biomimetic robotic fish,” IET Contr. Theory Appl. 3 (3), 281–293 (2009).CrossRefGoogle Scholar
22.Arena, P., “A Mechatronic Lamprey Controlled by Analog Circuits,” Proceedings of the 9th IEEE Mediterranean Conference on Control and Automation, Dubrovnik, Croatia (2001).Google Scholar
23.Zhou, C. and Low, K. H., “Kinematic Modeling Framework for Biomimetic Undulatory Fin Motion Based on Coupled Nonlinear Oscillators,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 934–939.Google Scholar
24.Wang, L., Wang, S., Cao, Z., Tan, M., Zhou, C., Sang, H. and Shen, Z., “Motion Control of a Robot Fish Based on CPG,” In: Proceedings of the IEEE International Conference Industrial Technology, Hong Kong (2005) pp. 1263–1268.Google Scholar
25.Wang, M., Yu, J. and Tan, M., “Modeling Neural Control of Robotic Fish with Pectoral Fins Using a CPG-Based Network,” In: Proceedings of the 46th IEEE Conference on Decision and Control (2009) pp. 6502–6507.Google Scholar
26.Lighthill, M. J., “Note on the swimming of slender fish,” J. Fluid Mech. 9 (3), 305–317 (1960).CrossRefGoogle Scholar
27.Mussa-Ivaldi, F. A. and Solla, S. A., “Neural primitives for motion control,” IEEE J. Oceanic Eng. 29 (3), 660–673 (2004).CrossRefGoogle Scholar
28.Pikovsky, A., Rosenblum, M. and Kurths, J., Synchronization, A Universal Concept in Nonlinear Sciences, vol. 12 (Cambridge University Press, Cambridge, UK, 2001).CrossRefGoogle Scholar
29.Righetti, L., Buchli, J. and Ijspeert, A., “Dynamic hebbian learning in adaptive frequency oscillators,” Physica D 216 (2), 269–281 (2006).CrossRefGoogle Scholar
30.Buchli, J., Iida, F. and Ijspeert, A. J., “Finding Resonance: Adaptive Frequency Oscillators for Dynamic Legged Locomotion,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 3903–3909.Google Scholar