УПРАВЛЕНИЕ МОБИЛЬНЫМ РОБОТОМ С ПРИМЕНЕНИЕМ НЕЙРОННОЙ СЕТИ ДЛЯ ПЛАНИРОВАНИЯ ДВИЖЕНИЯ В НЕКАРТОГРАФИРОВАННОЙ СРЕДЕ С ПРЕПЯТСТВИЯМИ

Abstract

In this work, a neural network of deep learning of a special structure is used. The neuralnetwork allows a mobile robot to move without encountering obstacles in an unknown environment.The main problems that the efforts of researchers in the field of neural network traffic plannersare aimed at solving are improving the performance of neural networks, optimizing theirstructure and automating learning processes. The main result of this article is a new iterative algorithmfor developing a training set. At the first iteration, the initial training set is developed andthe initial training of the neural network is performed. In the following iterations, the neural networktrained at the previous stage is used as a filter for the following training sets. The filter selectstrajectories with collisions caused by neural network errors. During the learning process, thenumber of convolutional and fully connected layers increases iteratively. Thus, the proposed algorithmmakes it possible to develop both a training set and a neural network architecture. Trainingresults are compared for filtered and unfiltered sets. The high efficiency of filtering has been confirmed,as a result of which the distribution of examples in the training sample changes. The algorithmcan be used to develop a planning block for a mobile ground control system. The articleprovides an example of training a neural network in a Matlab modeling environment. In the example,five iterations of training were carried out, during which an accuracy of more than 90% wasachieved. This accuracy was obtained using the collected statistics on the movement of the mobilerobot in a randomly generated environment. The density of filling the environment with obstacleswas up to 40%, which corresponds to urban conditions. The comparison of neural network plannerstrained using the proposed iterative procedure and with conventional training is carried out.The comparison showed that the use of an iterative procedure increases the accuracy of planningup to 12-15%. At the same time, the initial volume of the resulting sample is reduced several timesdue to the applied filtering.