Abstract
The objective of this paper is to biomimic a rectilinear swimming behaviour propulsion mechanism of a carangiform robotic fish with impressive speeds and show its exceptional characteristics in the fluid environment. Major kinematic parameters analysed from Lighthill (LH) mathematical model are chosen as major parameters to be adapted. Parameter information is drawn from a brain map similar to fish memory. Based on an environment based error signal robotic fish selects the proper parameter/s value showing adaptive behaviour. A DES methodology has been used to model the brain-kinematic-map, then integrated with a set-point control to track the brain map generated reference command for fish–tail undulation. Parameter adaptation for the three parameters have been shown to successfully emulate fish swimming. Joint-position tracking results are found to be satisfactory as the error converges in quick time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dewar, H., Graham, J.: Studies of Tropical Tuna Swimming Performance in a Large Water Tunnel—Kinematics. J. Exp. Biol. 192(1), 45–59 (1994) (CA, pp. 1–9, 1996)
Abhra, R.C., Prasad, B., Vishwanathan, V., Kumar, R., Panda, S.K.: Implementation of a BCF mode bio-mimetic robotic fish underwater vehicle based on Lighthill mathematical model. In: 2012 IEEE ICROS International Conference on Control, Automation and Systems, Korea, pp. 437–442
Metzner, W., Juranek, J.: A sensory brain map for each behavior? Proc. Natl. Acad. Sci. U.S.A. 94(26), 14798–14803 (1997)
Righetti, L., Ijspeert, A.J.: Programmable central pattern generators: an application to biped locomotion control. In: Proceedings of the 2006 IEEE BIOROB, 2006
Liu, A., Yu, Z.X., Ge, Z.K.: Gait transition of quadruped robot using time sequence control based on finite-state machine. Mechatron Robot Control Autom Manuf Appl Mech 24, 2799–2804
Wang, M., Yu, J., Tan, M., Zhang, G.: A CPG-based sensory feedback control method for robotic fish locomotion. In: Proceedings of the 30th Chinese Control Conference 22–24 July 2011, Yantai, China (2011)
Brandin B.A., Wonham W.M.: Modular supervisory control of timed discrete-event systems. In: Proceedings of the 32nd Conference on Decision and Control San Antonio, Texas Dec 1993 (1993)
Lighthill, M.J.: Note on the swimming of slender fish. J. Fluid Mech. 9, 305317 (1960)
Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, Hardcover (2011). ISBN 978-1-1199-9149-6, Kindle: avaialble at www.amazon.com
Abhra, R.C., Kumar, V., Prasad, B., Kumar, R., Panda, S.K.: Bio-harmonized dynamics model for a biology inspired carangiform robotic fish underwater vehicle. In: Proceedings of 19th IFAC World Congress, vol. 19, Capetown (2014)
Rooks, R.A.: How to build complete creatures rather than isolated cognitive simulators. In: Architectures for Intelligence, Conference Proceeding 22nd, Carnegie-Mellon University, Carnegie Symposium on Cognition (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chowdhury, A.R., Panda, S.K. (2014). Biomimicking a Brain-Map Based BCF Mode Carangiform Swimming Behaviour in a Robotic-Fish Underwater Vehicle. In: Ceccarelli, M., Glazunov, V. (eds) Advances on Theory and Practice of Robots and Manipulators. Mechanisms and Machine Science, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-07058-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-07058-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07057-5
Online ISBN: 978-3-319-07058-2
eBook Packages: EngineeringEngineering (R0)