Off-Road Terrain Traversability Analysis and Hazard Avoidance for UGVs

To achieve complete autonomy of unmanned ground vehicles (UGVs) in off-road terrain at high speeds, a robot must understand and analyze the terrain it is driving on in real-time just as a human analyzes the terrain and makes decisions of where to drive. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but there has yet to be seen a system that can accurately traverse offroad environments at high speeds in an autonomous fashion. We present algorithms that analyze the off-road terrain using a point cloud produced by a 3D laser range finder, determine potential hazards both above ground and those where the ground cover has a negative slope, then plan safe routes around those hazards. We take into account problems such as high-centering, tip-over, and overhanging obstacles. In this paper we discuss a real-time approach to analyzing the traversability of off-road terrain for UGVs considering positive and negative obstacles using multilevel surface maps and planning paths to a goal state.