Crash course learning: an automated approach to simulation-driven LiDAR-basedtraining 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 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.

Path Avoidance System of Intelligent Robot Based on Computer Vision

“…The robot's motion is controlled according to the direction of the force. This method has a simple structure and smooth planning path, but its performance is poor in narrow areas, and it is easy to fall into the local optimal situation where the resultant force of the robot is zero, and it cannot continue to move forward to obtain the globally optimal path [9][10].…”