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This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation. Once the best architecture is identified, it is trained using only data obtained in simulation and then implemented and tested on a real robot Turtlebot 2 in several simulations and real-world scenarios. Based on obtained results it is shown that this fast and simple approach is very powerful with good results in a variety of challenging environments, with both static and dynamic obstacles.

Crash course learning: an automated approach to simulation-driven LiDAR-basedtraining of neural networks for obstacle avoidance in mobile robotics