SureGrip: Perceptual Grasping of Natural Handholds for Free-Climbing Robots

Abstract

Exploration of steep and irregular terrains, such as lunar caves and vertical rock faces, requires free-climbing robots capable of identifying and securely grasping natural handholds. This study introduced SureGrip, a novel framework for detecting handholds and evaluating grasp quality in freeclimbing robots. By integrating depth-based contour extraction with gripper-specific contact analysis, SureGrip accurately identifies candidate handholds and quantifies their suitability using the proposed grasp metrics. Experimental results confirm that the framework can reliably detect handhold locations, estimate surface slopes, and distinguish between secure and unsuitable grasps across a range of artificial and natural surfaces. The findings emphasize the importance of both the number and placement of spine fingers for stable attachment. SureGrip thus enables informed handhold selection, improving climbing safety and efficiency.