LIMBERO: A Limbed Climbing Exploration Robot Toward Traveling on Rocky Cliffs

In lunar and planetary exploration, legged robots have attracted significant attention as an alternative to conventional wheeled robots, which struggle to traverse rough and uneven terrain. To enable locomotion over highly irregular and steeply inclined surfaces, limbed climbing robots equipped with grippers on their feet have emerged as a promising solution. In this paper, we present LIMBERO, a 10 kg-class quadrupedal climbing robot that employs spine-type grippers for stable locomotion and climbing on rugged and steep terrain. We first introduce a novel gripper design featuring coupled finger-closing and spine-hooking motions, tightly actuated by a single motor, which achieves exceptional grasping performance (>150 N) despite its lightweight design (525 g). Furthermore, we develop an efficient algorithm to visualize a geometry-based graspability index on continuous rough terrain. Finally, we integrate these components into LIMBERO and demonstrate its ability to ascend steep rocky surfaces under a 1 G gravity condition, a performance not previously achieved yet for limbed climbing robots of this scale.

3D Mapping Using a Lightweight and Low-Power Monocular Camera Embedded inside a Gripper of Limbed Climbing Robots

Limbed climbing robots are designed to explore challenging vertical walls, such as the skylights of the Moon and Mars. In such robots, the primary role of a hand-eye camera is to accurately estimate 3D positions of graspable points (i.e., convex terrain surfaces) thanks to its close-up views. While conventional climbing robots often employ RGB-D cameras as hand-eye cameras to facilitate straightforward 3D terrain mapping and graspable point detection, RGB-D cameras are large and consume considerable power. This work presents a 3D terrain mapping system designed for space exploration using limbed climbing robots equipped with a monocular hand-eye camera. Compared to RGB-D cameras, monocular cameras are more lightweight, compact structures, and have lower power consumption. Although monocular SLAM can be used to construct 3D maps, it suffers from scale ambiguity. To address this limitation, we propose a SLAM method that fuses monocular visual constraints with limb forward kinematics. The proposed method jointly estimates time-series gripper poses and the global metric scale of the 3D map based on factor graph optimization. We validate the proposed framework through both physics-based simulations and real-world experiments. The results demonstrate that our framework constructs a metrically scaled 3D terrain map in real-time and enables autonomous grasping of convex terrain surfaces using a monocular hand-eye camera, without relying on RGB-D cameras. Our method contributes to scalable and energy-efficient perception for future space missions involving limbed climbing robots. See the video summary here: https://youtu.be/fMBrrVNKJfc