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Abstract: In this paper, recent research efforts in the field of the application of neural networks (NNs) for the control of (semi-)autonomous underwater vehicles are reviewed. Based on a literature review the authors propose a classification of approaches to control underwater vehicles using NNs and the presented articles are categorized according to the identified categories. Based on practical results as described in the discussed literature this paper presents a qualitative assessment regarding the performance of the control strategies. Per category, or control strategy, the major advantages and disadvantages are identified and discussed. [Copyright &y& Elsevier]

This article describes experiments in enhancing the subsumption approach to behavior-based control as applied to a wheeled autonomous mobile robot. A modified or enhanced subsumption control approach is used, which reverses the behavior priority ordering between layers, and experimental results are reported. Modified behaviors employing fuzzy logic for command fusion are discussed and the testing of these demonstrates significant improvements in navigation around obstacles towards a goal. [ABSTRACT FROM AUTHOR]

Abstract: In this article the use of neural networks in the identification of models for underwater vehicles is discussed. Rather than using a neural network in parallel with the known model to account for unmodelled phenomena in a model wide fashion, knowledge regarding the various parts of the model is used to apply neural networks for those parts of the model that are most uncertain. As an example, the damping of an underwater vehicle is identified using neural networks. The performance of the neural network based model is demonstrated in simulations using the neural networks in a feed forward controller. The advantages of online learning are shown in case of noise impaired measurements and changing dynamics due to a change in toolskid. [Copyright &y& Elsevier]