UT-Planner: Energy-Efficient Trajectory Planner for Unmanned Ground Vehicles on Uneven Terrain

Internet-of-Things (IoT) sensing and V2X connectivity are expanding the deployment of electrified unmanned ground vehicles (UGVs) in rugged off-road environments. However, safe and energy-efficient navigation on uneven terrain remains challenging because terrain understanding, vehicle attitude prediction, and trajectory refinement are often handled in isolation. To address this limitation, this paper presents UT-Planner, an integrated energy- and stability-aware planning framework for IoT-enabled UGVs that exploits high-fidelity point-cloud maps. First, a wheel-contact-based attitude prediction module simulates four-wheel interactions on local point-cloud patches to estimate future body attitude and terrain roughness before traversal, thereby forming a predictive traversability layer. Second, an Eco-A∗ search augments Hybrid-A∗ with a Dubins-based energy heuristic that combines path length with terrain-aware energy surrogates to generate short and energy-favorable coarse paths. Third, a multi-objective trajectory optimizer refines the coarse path by jointly minimizing smoothness, gravity-aligned energy expenditure, and attitude deviation. Experiments on four representative uneven-terrain maps show that, relative to strong baselines, UT-Planner shortens the final 3D path length by 9.4%, reduces real energy consumption by 12.4%, improves trajectory smoothness by 13.3%, lowers the maximum tracking error by 19.8%, and also reduces peak body tilt. These results demonstrate a practical route to safe and energy-efficient off-road autonomous navigation.