Towards Proprioceptive Terrain Mapping with Quadruped Robots for Exploration in Planetary Permanently Shadowed Regions

TL;DR

Permanently Shadowed Regions (PSRs) pose navigation challenges due to darkness and rough terrain. The paper introduces a modular proprioceptive terrain mapping framework that builds a multi-layer 2.5D gridmap encoding elevation, foot slippage, Cost of Transport ($CoT$), and Gravito-Inertial margins (GIIM, GIAM) from online internal sensing, enabling robot-centered planning under low visibility. The approach is validated in a lunar-gravity simulator with the Aliengo quadruped, showing that maps capture terrain transitions and ground interaction patterns to inform path selection, while noting limitations of simulation-only validation. This work advances PSR exploration by integrating proprioceptive feedback with exteroceptive sensing to improve terrain assessment and planning fidelity.

Abstract

Permanently Shadowed Regions (PSRs) near the lunar poles are of interest for future exploration due to their potential to contain water ice and preserve geological records. Their complex, uneven terrain favors the use of legged robots, which can traverse challenging surfaces while collecting in-situ data, and have proven effective in Earth analogs, including dark caves, when equipped with onboard lighting. While exteroceptive sensors like cameras and lidars can capture terrain geometry and even semantic information, they cannot quantify its physical interaction with the robot, a capability provided by proprioceptive sensing. We propose a terrain mapping framework for quadruped robots, which estimates elevation, foot slippage, energy cost, and stability margins from internal sensing during locomotion. These metrics are incrementally integrated into a multi-layer 2.5D gridmap that reflects terrain interaction from the robot's perspective. The system is evaluated in a simulator that mimics a lunar environment, using the 21 kg quadruped robot Aliengo, showing consistent mapping performance under lunar gravity and terrain conditions.

Figures (5)

• Figure 1: Diagram of the proposed proprioceptive terrain mapping framework. The system processes raw proprioceptive signals to estimate terrain metrics, which are then integrated into a multi-layer gridmap.
• Figure 2: Simulation environments and robot. a) Symmetric ramps of variable inclination angle $\alpha$. b) Lunar terrain in OmniLRS with different curved trajectories and robot sensors.
• Figure 3: Layers of the proprioceptive terrain map collected in the structured ramp scenario. Each row shows: (a) Elevation, (b) Slippage detection, (c) CoT, (d) GIIM, (e) GIAM. Dashed vertical lines indicate transitions between slopes.
• Figure 4: Variation of proprioceptive metrics as a function of ramp inclination. The plot shows: CoT (red triangles), GIIM (blue circles) and GIAM (green squares)
• Figure 5: Terrain mapping in the OmniLRS environment. a) Lunar terrain with goal labels. b) Proprioceptive maps