Detection of Obstacle Features Using Neural Networks with Attention in the Task of Autonomous Navigation of Mobile Robots

Sviatov, Kirill; Miheev, Alexander; Sukhov, Sergey; Lapshov, Yuriy; Rapp, Stefan

This article describes the design process of a software package for image recognition of a mobile robot camera using neural networks with attention, which allows to identify the probability of a robot colliding with obstacles standing in its way. A key feature of this software is using a dataset that is prepared without manual labeling of all obstacles and the probability of a collision. Currently, an important task in mobile robotics is the need to use numerous heuristics and deterministic algorithms in control programs along with neural networks. The use of a single neural network that solves all the tasks of scene analysis (the so-called “end-to-end” solution) is impossible for several reasons: the high complexity of the training samples due to the large parameter space of the environment of the robot and the insufficient formalization of these parameters, as well as the computational complexity of machine learning algorithms, which is critical for mobile robots with strict energy requirements. Therefore, the development of a universal algorithm (end-to-end) is a laborious process. The article describes a method that allows to use weakly formalized parameters of the robot environment for training convolutional neural networks with attention using the obstacle recognition task. At the same time, weak formalization reduces the time-consuming process of manual data labeling due to automatically generated datasets in the NVIDIA Isaac environment, and the attention mechanism allows increasing the interpretability of the analysis results.