Modeling Collapse of Steered Vine Robots Under Their Own Weight

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.

On Steerability Factors for Growing Vine Robots

Vine robots extend their tubular bodies by everting material from the tip, enabling navigation in complex environments with a minimalist soft body. Despite their promise for field applications, especially in the urban search and rescue domain, performance is constrained by the weight of attached sensors or tools, as well as other design and control choices. This work investigates how tip load, pressure, length, diameter, and fabrication method shape vine robot steerability--the ability to maneuver with controlled curvature--for robots that steer with series pouch motor-style pneumatic actuators. We conduct two groups of experiments: (1) studying tip load, chamber pressure, length, and diameter in a robot supporting itself against gravity, and (2) studying fabrication method and ratio of actuator to chamber pressure in a robot supported on the ground. Results show that steerability decreases with increasing tip load, is best at moderate chamber pressure, increases with length, and is largely unaffected by diameter. Robots with actuators attached on their exterior begin curving at low pressure ratios, but curvature saturates at high pressure ratios; those with actuators integrated into the robot body require higher pressure ratios to begin curving but achieve higher curvature overall. We demonstrate that robots optimized with these principles outperform those with ad hoc parameters in a mobility task that involves maximizing upward and horizontal curvatures.

RubbleSim: A Photorealistic Structural Collapse Simulator for Confined Space Mapping

Despite well-reported instances of robots being used in disaster response, there is scant published data on the internal composition of the void spaces within structural collapse incidents. Data collected during these incidents is mired in legal constraints, as ownership is often tied to the responding agencies, with little hope of public release for research. While engineered rubble piles are used for training, these sites are also reluctant to release information about their proprietary training grounds. To overcome this access challenge, we present RubbleSim -- an open-source, reconfigurable simulator for photorealistic void space exploration. The design of the simulation assets is directly informed by visits to numerous training rubble sites at differing levels of complexity. The simulator is implemented in Unity with multi-operating system support. The simulation uses a physics-based approach to build stochastic rubble piles, allowing for rapid iteration between simulation worlds while retaining absolute knowledge of the ground truth. Using RubbleSim, we apply a state-of-the-art structure-from-motion algorithm to illustrate how perception performance degrades under challenging visual conditions inside the emulated void spaces. Pre-built binaries and source code to implement are available online: https://github.com/mit-ll/rubble_pile_simulator.