An Improved A* Path Planning Method for Unmanned Vehicles in Off-Road Environments Based on Geometric and Support Passability Analysis

To address the insufficient representation of terrain constraints and surface resistance in traditional path planning for off-road environments, this study proposes an improved A* path planning method for unmanned ground vehicles. First, an off-road environment model is constructed using Digital Elevation Model (DEM) and land cover data, and environment–vehicle traversability is evaluated by integrating geometric and support-based traversability analyses. Geometric constraints are determined using slope thresholds, minimum ground clearance, and approach/departure angles, while support-based traversability is quantified through a surface velocity influence coefficient to reflect traversal-efficiency differences under various surface conditions. These terrain and surface constraints are incorporated into the actual cost function of the A* algorithm, and a direction-corrected heuristic function is designed to enhance goal-directed search. Experiments conducted in Jiancaoping District, Taiyuan, show that, compared with the traditional A* algorithm, the proposed method reduces cumulative travel time, maximum path slope, and expanded nodes by 15.3%, 22.9%, and 47.8%, respectively, with only a 2.4% increase in path length. The results demonstrate that the proposed method effectively avoids steep and high-resistance areas while achieving coordinated optimization of path length, traversal efficiency, and terrain safety.