Modeling Collapse of Steered Vine Robots Under Their Own Weight

TL;DR

This paper introduces a comprehensive model to predict the collapse length of steered vine robots under their own weight, validated through experiments and applicable to various shapes and environments, enhancing 3D navigation capabilities.

Contribution

The work presents the first true shape-based collapse model for vine robots, enabling accurate prediction of collapse in complex environments and guiding successful navigation strategies.

Findings

Model accurately predicts collapse length in various shapes.

Validation shows the model's predictions match experimental results.

Application demonstrated in gap-crossing tasks with inflated actuators.

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.