Hierarchical Quadruped Navigation with Risk and Cost Evaluation: Simulation Validation on Unstructured Terrain for Lunar Missions
Year: 2026
Type: article
Abstract: 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.
Source: Aerospace
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225198032