Speed-invariant Terrain Roughness Classification and Control based on Inertial Sensors

Nowadays, it is notorious the increasing use of autonomous vehicles in different outdoor field applications such as agriculture, the mining industry, environmental monitoring, among others. In this context, the determination of the traversability level of a terrain is a fundamental task for safe and efficient navigation of Autonomous Ground Vehicles (AGVs) in unstructured unknown outdoor environments. Information like roughness of the ground is important when velocity control and other dynamic issues are concerned. However, most of the techniques in the literature use camera or lidar sensors to evaluate the ground around the robot, leading to complex and high cost systems. More simpler methods use only inertial sensors to estimate the roughness properties of the terrain, however they can be very sensitive to the robot's speed. In this paper, we propose a novel classifier capable of cluster different terrains based only on acceleration data provided by an Inertial Measurement Unit (IMU). We demonstrate with real-world experiments that, for different forward velocities, the mean accuracy of the classification exceeds 80%. Our method also incorporates a controller to regulate the speed according to the terrain identified by the robot in order to avoid abrupt movements over terrain changes.