提出可预测软体藤蔓机器人自重坍塌的模型，指导其跨越障碍。
Modeling Collapse of Steered Vine Robots Under Their Own Weight
- 基于真实形状与尾端拉力建模，预测机器人坍塌长度。
- 实验验证模型在无真形信息下仍能准确预测坍塌趋势。
- 适用于单驱动器导向机器人，指导实际越障任务设计。
受藤蔓启发的柔性生长机器人通过外翻运动在狭窄空间中高度机动，但在遭遇环境间隙时可能因自重而坍塌。本文提出一种全面的坍塌模型，利用真实形状信息和尾端张力，可预测任意形状下导向机器人的坍塌长度。通过未使用真形信息的无导向机器人实验验证，模型准确预测了实验趋势。随后测试了单驱动器导向机器人在不同姿态下的坍塌情况，模型在发生坍塌时均准确预测。最后通过机器人越隙任务演示：仅在充气驱动器时才能成功跨越而不坍塌，该结果与模型一致。模型针对由不可伸展材料制成的直型及串袋电机驱动机器人进行验证，但可推广至其他变体。本工作使我们能够在任意开放环境中建模机器人坍塌行为，并理解其在三维导航任务中成功所需的关键参数。
原文摘要 · Abstract (English)
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.
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