An Enhanced Soft Growing Robot with Mixed-Layer Jamming for Superior Load Capacity and Improved Mobility
Zheyu Li, Kui Sun, XueAi Li, Yanjiang Zou, Hong Liu
AI summary
Problem
Conventional layer jamming in soft growing robots fails to balance high load-bearing capacity with smooth tip mobility due to high curvature interference and rigid fixation methods.
Approach
The authors design a mixed-layer jamming structure combining sandpaper and printer paper, replace tape fixation with heat-sealing to reduce deformation resistance, and formulate new kinematic and tip statics models.
Key results
- More than double the load-bearing capacity compared to baseline designs
- 9% reduction in tip growth energy consumption and mechanical resistance
- 17% improvement in tip retraction capability
- 41.2% enhancement in kinematic model prediction accuracy
Why it matters
This design enables soft growing robots to reliably carry payloads and navigate unstructured environments, advancing their practical use in exploration and medical applications.
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 mate- rial 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 letter, 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 fabrica- tionmethod.Furthermore,weestablishamoreaccuratekinematics 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).