Artifi cial Intelligence Technologies in the Autonomous Underwater Vehicle Control Systems

Abstract

The purpose of the paper is to present the experience of developing algorithms implemented in the AUV control system using artificial intelligence (AI) technologies. The author’s view on the content of the concept of AI in relation to the creation of complex technical systems is presented. Two of the most promising, according to the author, AI technologies are identified: 1) development of the basic algorithm and its improvement based on the results of comprehensive modeling in various operating conditions; 2) creation of a problem-oriented artificial neural network and its deep learning using a large amount of experimentally obtained training material. It is stated that both technologies are quite time-consuming and require a long time to implement. But if the completeness and adequacy of modeling of the conditions in which the system being created is supposed to function plays a key role in modeling technology, then in machine learning technology, the availability of a sufficient amount of training material comes to the fore (in the case of developing vision systems — images of recognizable objects, the number of which can number many thousands).The paper presents the structure of the multi-agent control system of AUV, focuses on the complexity of the tasks it solves and the need to use AI technologies in its creation. It is shown that of all the tasks solved by the AUV control system, AI methods are most in demand for solving two categories of tasks: 1) recognition of the current situation and making an adequate decision in this situation in the interests of completing the route task; 2) search for the designated bottom object among many other bottom objects of natural and artificial origin. The use of AI technologies is demonstrated by the example of the development using a specially created simulation stand of the AUV control algorithm when bypassing an extended. It is proposed to solve the problem of detecting and recognizing a designated bottom object using deep learning technology of a problem-oriented artificial neural network with the peculiarity that the training material is formed programmatically in the form of digital images of the desired bottom object at the output of hydroacoustic, optical and electromagnetic means of monitoring the bottom in various conditions of their observation.