A Tool for Mass Generation of Random Step Environment Models with User-Defined Landscape Features

Abstract

Computer simulations are growing in popularity in robotics research due to their near-zero cost of error and lower labor intensity. One of necessary components of a simulation, in addition to a robot model, is a model of a world in which the robot operates. While it is always possible to construct a world model manually, a demand for automatic tools that generate multiple testing environments with particular user-defined features grows together with integration of data hungry machine learning techniques into robotic algorithms. This article presents a next generation of LIRS-RSEGen tool for constructing virtual random step environments (RSE). The new tool can simultaneously generate multiple RSE models with user-defined specific features that are declared via an intuitive graphical user interface. The resulting models simulate an urban search and rescue environment and can be used with robot models for developing and testing software for localization, mapping, navigation and locomotion, and are applicable for machine learning due to their relatively low impact on performance and random elements in RSE generation. The constructed worlds’ performance was successfully tested with robot models in the Webots and Gazebo simulators.