Enhancing Wheeled Robot Autonomy in Off-Road Terrain through Data-Driven AI Models

Abstract

Wheeled robots have incredible potential to improve disaster response, military operations, and even the exploration of extraterrestrial bodies, through their mobility, durability, and decreased direct human interaction. However, they are currently designed for controlled environments with easily traversable terrain. Most wheeled robots face a significant disadvantage in off-road environments, because they are unable to traverse parts of the terrain such as rough patches or slopes. This also affects the possibility of developing fully autonomous off-road vehicles as the chance of failure on the terrains is very likely. To address this limitation, we developed three small scale wheeled robots, called “Verti-Wheelers”, to gather data both about their environment and the manual controls used to navigate them. We drove these robots in a controlled off-road ‘arena’ to collect data, and gathered almost 100 GB of RGB (color) images, depth images, odometry, and manual throttle/steering movement from the controller. We intend on using this data to train AI models to reduce the need for direct human interaction and increase autonomous capabilities of the Verti-Wheelers. This work lays the foundation for developing more intelligent and adaptable wheeled robots capable of navigating challenging off-road environments with greater autonomy.