HyperNEAT-based flipper control for a crawler robot motion in 3D simulation environment

Abstract

The paper presents neuroevolution approach to a crawler robot motion that autonomously solves sequences of navigation and flipper control tasks to overcome obstacles in 3D simulation domain. When modelling scenarios of robot locomotion, we used our model of a novel Russian crawler robot “Engineer” in ROS/Gazebo. The modelled robot measured obstacle's height by scanning a vertical profile of a terrain with 2D LIDAR and moved through 3D environment, adjusting its flippers to a relief by commands of the HyperNEAT neural network. As the result, our neuroevolution method was trained and tested on simulated data with a set of obstacles, demonstrating original solutions to robot navigation in 3D scene.