Adaptive Traversability Policy Optimization for an Unmanned Articulated Road Roller on Slippery, Geometrically Irregular Terrains

To address the autonomous traversability challenge of an Unmanned Articulated Road Roller (UARR) operating on harsh terrains where low-adhesion slipperiness and geometric irregularities are coupled, and traction capacity is severely limited, this paper proposes a Terrain-Adaptive Maximum-Entropy Policy Optimization (TAMPO). A unified multi-physics simulation platform is constructed, integrating a high-fidelity vehicle dynamics model with a parameterized terrain environment. Considering the prevalence of geometric irregularities in construction sites, a parameterized mud-pit model is established—generalized from a representative case—as a canonical physical model and simulation carrier for this class of traversability problems. Based on this model, a family of training and test scenarios is generated to span a broad range of terrain shapes and adhesion conditions. On this foundation, the TAMPO algorithm is introduced to enhance vehicle traversability on complex terrains. The method comprises the following: (i) a Terrain Interaction-Critical Reward (TICR), which combines dense rewards representing task progress with sparse rewards that encourage terrain exploration, guiding the agent to both climb efficiently and actively seek high-adhesion favorable terrain; and (ii) a context-aware adaptive entropy-regularization mechanism that fuses, in real time, three feedback signals—terrain physical difficulty, task-execution efficacy, and model epistemic uncertainty—to dynamically regulate policy entropy and realize an intelligent, state-dependent exploration–exploitation trade-off in unstructured environments. The performance and generalization ability of TAMPO are evaluated on training, interpolation, and extrapolation sets, using PPO, SAC, and DDPG as baselines. On 90 highly challenging extrapolation scenarios, TAMPO achieves an average success rate (S.R.) of 60.00% and an Average Escape Time (A.E.T.) of 17.56 s, corresponding to improvements of up to 22.22% in S.R. and reductions of up to 5.73 s in A.E.T. over the baseline algorithms, demonstrating superior decision-making performance and robust generalization on coupled slippery and irregular terrains.