Hierarchical Quadruped Navigation with Risk and Cost Evaluation: Simulation Validation on Unstructured Terrain for Lunar Missions

Quadruped navigation for future lunar missions requires route selection over unstructured terrain that accounts for terrain characteristics and controller execution capabilities. Fixed geometric rules cannot fully capture controller-specific execution risks and time costs. We propose hierarchical navigation based on risk and execution cost evaluation, using regional elevation maps and controller closed-loop data to build local execution and subsequent route-time models. These predict the execution risk and execution time of reaching candidate local goals and the subsequent route time to the task goal, respectively. A deterministic selector combines both stages’ time costs under tiered risk constraints to select each local goal, and a residual local planner tracks the selected goal in closed loop. Upon local-goal arrival, selection repeats until the task goal is reached. Simulations under terrestrial gravity and rigid contact use two low-level controllers to evaluate navigation performance and adaptability to controller changes. The configuration using the first controller achieves 100% success across 108 episodes comprising 36 unstructured-terrain tasks and three evaluation seeds. Ablations show that local execution-risk prediction, subsequent route evaluation, and tiered risk-constrained selection contribute to task completion. With the navigation architecture and hyperparameters fixed, the system adapts to another controller by collecting new closed-loop data and constructing controller-specific models, achieving 93.52% success and producing different detour and obstacle-crossing routes on the same terrain. The results show that the proposed method enables global route selection to adapt to controller-specific execution capabilities, providing a basis for adaptation to lunar locomotion controllers and navigation validation under lunar conditions.