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The article describes an approach to the implementation of a software package for determining the probability of a collision of a mobile robot with obstacles. This approach is based on a neural network model with attention. Key feature is the method of the training dataset generation: the labeling of obstacles and the values of the probability of collision with them is performed not in manual mode, but using a deterministic algorithm that uses the result of semantic segmentation using another pre-trained neural network. This method allows to use a poorly detailed description of the external environment for training convolutional neural networks with attention on the example of recognizing obstacles when a mobile robot moves in simulation mode. At the same time, low detail allows to reduce the time-consuming process of manual data labeling due to automatically generated sampling in the NVIDIA Isaac environment, and the attention mechanism allows to increase the interpretability of the analysis results.

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