CONTROL OF A MOBILE ROBOT ON BASE OF NEURAL NETWORK FOR THE PATH PLANNING IN UNMAPPED OBSTRUCTED ENVIRONMENT
Abstract
In this work, a neural network of deep learning of a special structure is used. The neural
network 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 planners
are aimed at solving are improving the performance of neural networks, optimizing their
structure and automating learning processes. The main result of this article is a new iterative algorithm
for developing a training set. At the first iteration, the initial training set is developed and
the initial training of the neural network is performed. In the following iterations, the neural network
trained at the previous stage is used as a filter for the following training sets. The filter selects
trajectories with collisions caused by neural network errors. During the learning process, the
number of convolutional and fully connected layers increases iteratively. Thus, the proposed algorithm
makes it possible to develop both a training set and a neural network architecture. Training
results 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 algorithm
can be used to develop a planning block for a mobile ground control system. The article
provides 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% was
achieved. This accuracy was obtained using the collected statistics on the movement of the mobile
robot in a randomly generated environment. The density of filling the environment with obstacles
was up to 40%, which corresponds to urban conditions. The comparison of neural network planners
trained 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 planning
up to 12-15%. At the same time, the initial volume of the resulting sample is reduced several times
due to the applied filtering.
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