Procedural generation of tunnel networks for unsupervised training and testing in underground applications

Abstract

Developing a robotic application requires thorough testing of the complete system to ensure its reliability. However, depending on the target environment, real-life testing can be difficult to carry out, which favors simulations. Also, some techniques like those based on machine learning, may require large varieties of sensor data, which can be gathered in simulation with ease, whereas doing the same in real environments can pose a great challenge.This work presents a flexible approach to the procedural generation of tunnel networks suitable for underground robotics simulations. The method starts with a graph representation of an underground environment, and applies a custom meshing strategy to generate tunnels that follow the graph structure. This mesh can then be imported into the desired simulation software. The ease of use of this method allows for the testing of robotic applications in an arbitrary number of different environments in completely automated workflows.