An Enhanced Soft Growing Robot with Mixed-Layer Jamming for Superior Load Capacity and Improved Mobility

Abstract

Soft robots have gained widespread attention due to their lightweight nature and inherent safety. Among them, soft growing robots (SGRs) are inspired by the growth mechanism of vines, achieving movement through tip eversion. However, their load-bearing capacity remains a significant challenge due to material limitations. The stiffness modulation approach based on layer jamming is constrained in high-curvature tip regions, preventing it from fully exhibiting its potential in unstructured environments. In this paper, motivated by enhancing the load-bearing capacity of SGR and optimizing their tip motion performance, we propose a novel mixed-layer soft growing robot (MLSGR) and introduce an innovative modification to the conventional layer jamming fabrication method. Furthermore, we establish a more accurate kinematics model and, for the first time, propose a statics model to characterize tip behavior. Experimental results demonstrate that, compared to previous work, MLSGR exhibits more than twice in load capacity, a 9% reduction in energy consumption and mechanical resistance for tip growth, a 17% improvement in tip retraction capability, and a 41.2% enhancement in kinematic model prediction accuracy (MAPE).