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

2025-10-21

AI Summary

To address the challenge of autonomous exploration in permanently shadowed regions (PSRs) of the lunar poles—characterized by complete darkness and highly complex terrain—this paper proposes an ontology-aware, multi-dimensional terrain interaction mapping framework. The method leverages only onboard inertial measurements, joint torque, and pose sensing data from a quadrupedal robot, integrating motion dynamics modeling with incremental state estimation to concurrently infer terrain elevation, foot slippage magnitude, local energy consumption, and stability margin in real time, thereby constructing a multi-layered 2.5D grid map. No external sensors or prior terrain knowledge are required. Validated in high-fidelity lunar-gravity and representative lunar-terrain simulations, the framework enables robust mapping for a 21-kg Aliengo robot. All terrain metrics converge consistently, supporting embodied terrain understanding, autonomous navigation, and risk assessment for PSR missions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Mapping terrain interaction metrics for quadruped robots
• Estimating elevation, slippage, energy cost, and stability
• Enabling exploration in challenging lunar shadowed regions

Innovation

• Proprioceptive sensing estimates terrain interaction metrics
• Multi-layer gridmap integrates elevation and locomotion data
• System validated in lunar gravity simulation with quadruped robot