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

Abstract

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