Quadruped robots map and navigate rough lunar terrain autonomously
Terrain-Aware Autonomous Planetary Exploration for Exteroceptive-Proprioceptive Mapping with Quadruped Scouts
Robotics
Summary
Exploring unknown planetary surfaces can be tricky because robots have to walk over rough ground safely and efficiently. This paper presents a way for a four-legged robot to create detailed maps using cameras and its own movement sensors. These maps help the robot figure out where it can safely walk while spending less energy. The system combines both what the robot sees and feels to better understand the terrain. Testing in simulations showed the robot could explore and move around while using less energy than before.
What this means in practice
- •For space robotics teams: Enable planetary exploration robots to autonomously map and traverse uneven lunar-like terrains using combined visual and proprioceptive data.
- •For search and rescue operators: Support ground robots in navigating complex and hazardous environments by integrating visual and tactile terrain information for safer movement.
Authors
Alberto Sanchez-Delgado, João Carlos Virgolino Soares, Victor Barasuol, Claudio Semini
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.