提出可并行模拟软体生长机器人的接触与驱动，用于复杂环境导航设计优化。
Parallel Simulation of Contact and Actuation for Soft Growing Robots
- 构建统一模型，融合生长、弯曲、驱动与障碍接触的力学机制。
- 实现快速并行仿真，支持在杂乱环境中优化机器人设计。
- 实测验证设计鲁棒性，减少所需驱动器数量并成功部署于真实场景。
软体生长机器人（又称藤蔓机器人）在非结构化动态环境中表现出优异的安全性与鲁棒性，因此利用与环境的接触进行路径规划与设计优化具有重要意义。以往研究主要针对预设弯折的被动变形机器人进行接触下的路径规划，但要在复杂环境中成功导航，需引入主动转向能力。为此，本文开发了一套统一建模框架，整合藤蔓机器人的生长、弯曲、驱动及障碍接触行为。通过扩展梁力矩模型以包含驱动对生长过程中的运动学影响，并基于此建立快速并行仿真系统。我们通过真实机器人实验验证了模型与仿真器的有效性。为展示框架能力，进一步应用于设计优化任务，在杂乱环境中寻找最小驱动器数量的机器人设计方案，充分利用环境接触实现高效导航。结果表明设计对环境与制造不确定性具有强鲁棒性。最后，我们制作了优化后的原型，并成功部署于障碍密集的真实环境。
原文摘要 · Abstract (English)
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.
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