Abstract
Autonomous underwater vehicles (AUVs) have great advantages for activities in deep oceans, and are expected as the attractive tool for near future underwater development or investigation. However, AUVs have various problems which should be solved for motion control, acquisition of sensors’ information, behavioral decision, navigation without collision, self-localization and so on. This paper proposes an adaptive biologically inspired neural controller for trajectory tracking of AUVs in nonstationary environment. The kinematic adaptive neuro-controller is an unsupervised neural network, which is termed Self-Organization Direction Mapping Network (SODMN). The network uses an associative learning system to generate transformations between spatial coordinates and coordinates of propellers’ velocity. The neurobiological inspired control architecture requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The SODMN proposed in this paper represents a simplified way to understand in part the mechanisms that allow the brain to collect sensory input to control adaptive behaviours of autonomous navigation of the animals. The efficiency of the proposed neurobiological inspired controller for autonomous intelligent navigation was implemented on an underwater vehicle capable of operating during large periods of time for observation and monitoring tasks.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Seshadri, C., Ghosh, A.: Optimum path planning for robot manipulators amid static and dynamic obstacles. IEEE Trans. Syst. Man, Cybern. 23, 576–584 (1993)
Li, Z.X., Bui, T.D.: Robot path planning using fluid model. J. Intell. Robot. Syst. 21, 29–50 (1998)
Oriolo, G., Luca, A.D., Vendittelli, M.: WMR control via dynamic feedback linearization: Design, implementation and experimental validation. IEEE Trans. Control. Syst. Technol. 10, 835–852 (2002)
Fujii, T., Arai, Y., Asama, H., Endo, I.: Multilayered reinforcement learning for complicated collision avoidance problems. In: Proceedings IEEE International Conference on Robotics and Automation, Leuven, Belgium, vol. 3, pp. 2186–2191 (1998)
Carreras, M., Yuh, J., Batlle, J., Ridao, P.: A behavior-based scheme using reinforcement learning for autonomous underwater vehicles. IEEE Journal Of Oceanic Engineering 30, 416–427 (2005)
Ritter, H.J., Martinez, T.M., Schulten, K.J.: Topology-conserving maps for learning visuo-motor coordination. Neural Networks 2, 159–168 (1989)
García-Córdova, F.: A cortical network for control of voluntary movements in a robot finger. Neurocomputing 71, 374–391 (2007)
García-Córdova, F., Guerrero-González, A., Marín-García, F.: Design and implementation of an adaptive neuro-controller for trajectory tracking of nonholonomic wheeled mobile robots. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 459–468. Springer, Heidelberg (2007)
Gaudiano, P., Grossberg, S.: Vector associative maps: Unsupervised real-time error-based learning and control of movement trajectories. Neural Networks 4, 147–183 (1991)
Baraduc, P., Guigon, E., Burnod, Y.: Recording arm position to learn visuomotor transformations. Cerebral Cortex 11, 906–917 (2001)
Georgopoulos, A.P.: Neural coding of the direction of reaching and a comparison with saccadic eye movements. Cold Spring Harbor Symposia in Quantitative Biology 55, 849–859 (1990)
Caminiti, R., Johnson, P., Urbano, A.: Making arm movements within different parts of space: Dynamic aspects in the primate motor cortex. Journal of Neuroscience 10, 2039–2058 (1990)
Rondot, P., De-Recondo, J., Dumas, J.: Visuomotor ataxia. Brain 100, 355–376 (1976)
Lacquaniti, F., Guigon, E., Bianchi, L., Ferraina, S., Caminiti, R.: Representing spatial information for limb movement: Role of area 5 in the monkey. Cerebral Cortex 5, 391–409 (1995)
Fiala, J.C.: Neural Network Models of Motor Timing and Coordination. PhD thesis, Boston University (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Córdova, F., Guerrero-González, A. (2011). A Biologically Inspired Neural Network for Autonomous Underwater Vehicles. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-21501-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21500-1
Online ISBN: 978-3-642-21501-8
eBook Packages: Computer ScienceComputer Science (R0)