AUV robot's real-time control navigation system using multi-layer neural networks management
Date
2011
Authors
Anvar, A.
Anvar, A.
Editors
Chan, F.
Marinova, D.
Anderssen, R.S.
Marinova, D.
Anderssen, R.S.
Advisors
Journal Title
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Volume Title
Type:
Conference paper
Citation
Proceedings of the 19th International Congress on Modelling and Simulation (MODSIM2011), 12 to 16 December 2011, Perth, Western Australia / F. Chan, D. Marinova and R. S. Anderssen (eds.): pp.277-283
Statement of Responsibility
Amir M. Anvar and Amir P. Anvar
Conference Name
International Congress on Modelling and Simulation (19th : 2011 : Perth, Australia)
Abstract
This paper describes the detection and tracking of static and dynamic underwater object(s). It addresses the case study application of a multi-layer artificial neural network prototype model on the bases of an analytical approach. It supports an Autonomous Underwater Vehicle (AUV) robot’s controller system with automated detection of processed-obstacle-signals. The significance of this work is to investigate the neural network learning perception process of signal detection within operational environments. In this case, the acoustic-sound density is the source of detection and classification processes. The outcomes of this work are presented as simulated results that illustrate the error-detection control system. It activates due to a range of training forces originating from encountered acoustic-sensors’ signals. In addition, the benefit of further simulation of the proposed technique can provide sufficient knowledge on the set-up of the controller’s cyclic triggering towards actuators. The other benefits are included with control overshoot and rotational alignment of thrusters for precise navigational trajectory in real-time.
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Copyright © 2011 The Modelling and Simulation Society of Australia and New Zealand Inc. All rights reserved.