Adaptive Terrain Mapping and Classification for Off-Road UGV Navigation

Abstract

Off-road navigation poses significant challenges for Unmanned Ground Vehicles (UGVs) due to the unpredictability of terrain conditions and the need for real-time adaptation. This paper introduces an adaptive terrain mapping and classification framework for off-road UGVs, enabling precise navigation in dynamic and unstructured environments. The proposed system combines LIDAR, cameras, and inertial sensors to gather real-time terrain data, which is then processed through machine learning algorithms for terrain classification. The system distinguishes between different terrain types, such as gravel, mud, grass, and rocks, allowing the UGV to adjust its navigation strategies accordingly. Our approach enhances both the safety and efficiency of UGVs by optimizing traction control, obstacle avoidance, and path planning in challenging off-road conditions. Field experiments validate the system’s ability to adapt to changing terrains, demonstrating improved performance in environments such as forests, deserts, and mountainous regions. The integration of adaptive terrain mapping techniques ensures reliable UGV operation in diverse off-road applications.