AN ALGORITHM FOR CONTROLLING AN AUTONOMOUS UNDERWATER VEHICLE WHEN SEARCHING FOR A DESIGNATED BOTTOM OBJECT WITH THE INTEGRATED USE OF VARIOUS BOTTOM MONITORING TOOLS
Abstract
The search for designated bottom objects is one of the most difficult tasks solved by the AUV, due to a number of factors, the main of which are: the variety of search objects (sunken submarines, surface ships, airplanes, helicopters, mines, underwater pipelines and communication cables, various underwater infrastructure), the need for integrated use for search for various bottom monitoring tools that differ in their physical principles of operation, resolution, and search performance. The purpose of the work, the results of which are presented in the article, was to develop an algorithm for managing the AUV when searching for a designated bottom object that meets these requirements, and to verify it using a digital polygon and a digital twin of the AUV. The probability of correctly attributing the detected bottom object to the search object was chosen as a criterion for choosing a bottom monitoring tool in each specific case. As a result, the logic of searching for a designated bottom object is as follows. The search for bottom objects is carried out using a tool with maximum search performance. When a bottom object is detected, the probability of its belonging to the search object is determined. If it exceeds the set high threshold, a decision is made to locate the designated bottom object. If it is less than the specified low threshold, it is decided that an extraneous object has been detected. In other cases, a decision is made on the need to examine the object with a higher resolution. The technology of classification of bottom objects based on the training of an artificial neural network trained using synthesized training material is described. The results of checking the effectiveness of the developed algorithm using a digital polygon and a digital twin of AUV are presented. The simulation of the developed algorithm showed that the integrated use of bottom monitoring tools increases the likelihood of a successful solution to the problem and reduces the time needed to solve it.
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