Terrain-Aware Heuristic for Hybrid A*: Low-Cost Path Planning in Off-Road Environments

Path planning for unmanned ground vehicles (UGVs) in off-road environments is hindered by unstructured terrains and the trade-off between kinematic feasibility and computational efficiency. To address this issue, a terrain-aware heuristic-based hybrid A∗ path planning method is proposed. First, a multi-layer terrain traversability cost map is constructed by fusing obstacle, slope and self-supervised learning-based bumpiness cost layers, providing comprehensive terrain information. Then, a terrain-aware A∗ algorithm generates an initial reference path, which is further embedded into an adaptively fused heuristic function for the hybrid A∗ algorithm. This design retains vehicle kinodynamic constraints while reducing computational complexity by leveraging terrain guidance. Real-world experiments on a UGV platform demonstrate that the proposed method outperforms standard HA*, T-HA∗ and SC-HA∗ in balancing terrain adaptability, path feasibility and planning efficiency, with the shortest computational time and fewer expanded nodes while achieving low terrain cost.