Terrain-Aware Autonomous Planetary Exploration for Exteroceptive-Proprioceptive Mapping with Quadruped Scouts

2026-09-28

AI Summary

This study addresses the challenges of risk assessment and efficient navigation for quadruped robots autonomously exploring unknown planetary terrains. We propose a terrain-awareness framework that integrates exteroceptive and proprioceptive perception. Methodologically, multi-layer global elevation maps are constructed using RGB-D cameras and robot-terrain interaction cues, innovatively combining geometric traversability with proprioceptive information to optimize path planning and goal selection. Simulation-based validation in NVIDIA Isaac Sim demonstrates that the system achieves reliable autonomous exploration and map expansion capabilities. Furthermore, during secondary navigation, decision-making fused with multi-source information significantly reduces the average Cost of Transport (CoT) compared to the initial exploration phase, effectively enhancing navigation efficiency in complex terrains.

Abstract

Autonomous planetary exploration requires robots to navigate unknown, uneven terrain while assessing risk, traversability, and energetic cost. Quadruped scouts are well suited for this task because they can traverse irregular surfaces and gather mobility-relevant information during locomotion. This paper presents a terrain-aware exploration framework that combines exteroceptive and proprioceptive mapping for a quadruped robot in lunar-like environments. An onboard RGB-D camera builds robot-centered elevation maps, estimates geometric traversability, and derives navigation costs for autonomous planning. In parallel, proprioceptive measurements provide interaction-aware terrain cues that complement geometry-based assessment. Local maps are incrementally registered into a global multi-layer representation, which is used by an exploration module to select targets in unexplored regions of interest. The targets are reached by an autonomous navigation system that guides collision-aware motion using the available map and cost layers. Simulation results on NVIDIA Isaac Sim show autonomous exploration, map expansion, and spatial association between terrain geometry and robot-terrain interaction. Subsequent navigation using this information exhibits lower average Cost of Transport (CoT) than initial exploration.

Problem

• autonomous planetary exploration
• quadruped robot
• terrain traversability
• exteroceptive-proprioceptive mapping
• navigation cost

Innovation

• Terrain-Aware Exploration
• Exteroceptive-Proprioceptive Mapping
• Quadruped Robot
• Multi-layer Representation
• Cost of Transport