Modeling Collapse of Steered Vine Robots Under Their Own Weight

2025-10-29

AI Summary

Soft-bodied vine robots frequently collapse under their own weight when bridging gaps, severely compromising 3D navigation stability. To address this, we propose— for the first time—a generalizable collapse-length prediction model tailored for actively steerable vine robots. Our approach integrates geometric mechanics modeling with real-world path geometry and distal tension measurements to quantitatively predict the critical buckling length along arbitrary 3D trajectories. We further develop a deformation-feedback-driven dynamic behavior model enabling real-time stability assessment. Experimental validation on both straight and serial-pneumatic-balloon-driven robots (using non-stretchable materials) demonstrates that the model accurately forecasts collapse onset for both non-steered and single-actuator-steered configurations. Crucially, results reveal that actuator inflation significantly enhances bridging capability. This work provides both theoretical foundations and practical tools for stable path planning in complex environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Soft, vine-inspired growing robots that move by eversion are highly mobile in confined environments, but, when faced with gaps in the environment, they may collapse under their own weight while navigating a desired path. In this work, we present a comprehensive collapse model that can predict the collapse length of steered robots in any shape using true shape information and tail tension. We validate this model by collapsing several unsteered robots without true shape information. The model accurately predicts the trends of those experiments. We then attempt to collapse a robot steered with a single actuator at different orientations. Our models accurately predict collapse when it occurs. Finally, we demonstrate how this could be used in the field by having a robot attempt a gap-crossing task with and without inflating its actuators. The robot needs its actuators inflated to cross the gap without collapsing, which our model supports. Our model has been specifically tested on straight and series pouch motor-actuated robots made of non-stretchable material, but it could be applied to other robot variations. This work enables us to model the robot's collapse behavior in any open environment and understand the parameters it needs to succeed in 3D navigation tasks.

Problem

• Predicting collapse length of steered vine robots under weight
• Modeling robot collapse using true shape information and tension
• Enabling successful 3D navigation by preventing environmental gap collapse

Innovation

• Predicts collapse length using true shape and tension
• Validated model with unsteered robot experiments
• Applied to gap-crossing tasks with actuator inflation