Fuzzy Terrain Traversability Assessment and Adaptive Artificial Potential Field for Unmanned Ground Vehicle Control Based on Light Detection and Ranging

Unmanned ground vehicle control in unknown environments is hindered by terrain variation, obstacle complexity, and sensor noise. This study proposes a light-detection-and-ranging-based control system that integrates fuzzy terrain traversability assessment (FTTA) and adaptive artificial potential field (AAPF) algorithms for terrain detection and obstacle avoidance. Cloth simulation filtering (CSF) is used to preprocess point clouds for distinguishing between terrain and aboveground points. The FTTA algorithm then identifies terrain obstacles using slope and height features, with aboveground points classified as obstacles by height. Moreover, the AAPF algorithm integrates both terrain and aboveground points to adaptively adjust the exclusion radius and improve path planning. Experimental analysis revealed that FTTA with CSF achieved a ground point retention rate of 0.682, a height variance of 0.087, and a processing time of 0.016 s. These values maintained the slope estimation angles close to the true values and ensured stable and rapid computation. In navigation experiments, the AAPF algorithm achieved a reduced travel distance of 62.4 ± 3.2 m and a reduced travel time of 154 ± 5 s. Compared with the baseline APF method, this represents a reduction of 21% in navigation time, indicating improved efficiency and stability.