Soft growing robots, commonly referred to as vine robots, have demonstrated remarkable ability to interact safely and robustly with unstructured and dynamic environments. It is therefore natural to exploit contact with the environment for planning and design optimization tasks. Previous research has focused on planning under contact for passively deforming robots with pre-formed bends. However, adding active steering to these soft growing robots is necessary for successful navigation in more complex environments. To this end, we develop a unified modeling framework that integrates vine robot growth, bending, actuation, and obstacle contact. We extend the beam moment model to include the effects of actuation on kinematics under growth and then use these models to develop a fast parallel simulation framework. We validate our model and simulator with real robot experiments. To showcase the capabilities of our framework, we apply our model in a design optimization task to find designs for vine robots navigating through cluttered environments, identifying designs that minimize the number of required actuators by exploiting environmental contacts. We show the robustness of the designs to environmental and manufacturing uncertainties. Finally, we fabricate an optimized design and successfully deploy it in an obstacle-rich environment.

翻译：软体生长机器人，通常称为藤蔓机器人，已展现出与无结构和动态环境安全稳健交互的显著能力。因此，利用与环境的接触进行规划与设计优化任务具有天然优势。先前的研究主要集中于针对具有预成型弯曲的被动变形机器人进行接触条件下的规划。然而，为这些软体生长机器人添加主动转向能力对于在更复杂环境中成功导航是必要的。为此，我们开发了一个统一建模框架，集成了藤蔓机器人的生长、弯曲、驱动与障碍物接触。我们扩展了梁矩模型，以纳入生长条件下驱动对运动学的影响，并利用这些模型开发了快速并行仿真框架。我们通过真实机器人实验验证了模型与仿真器的有效性。为展示本框架的能力，我们将模型应用于设计优化任务，以寻找在杂乱环境中导航的藤蔓机器人设计方案，通过利用环境接触识别出能最小化所需驱动器数量的设计。我们展示了设计方案对环境与制造不确定性的鲁棒性。最后，我们制造了一个优化设计并成功将其部署在障碍物密集的环境中。