arXiv:2603.09473cs.ROcond-mat.mtrl-sci2026-03

用血管化结构在机器人内部原位生成传感器,实现自适应调控。

Receptogenesis in a Vascularized Robotic Embodiment

  • 通过流体输送前驱体并配合局部紫外光固化,实现材料的原位合成。
  • 成功构建可检测电导变化的传感器,实时调控仿蛾机器人翅膀拍打。
  • 为具备自生成硬件能力的智能机器人提供新思路,适合自适应系统研究者。

赋予机器人在运行过程中原位生成硬件的能力,可显著提升其物理适应性。与依赖预装或事后组装的模块化系统不同,本工作提出通过体内连续材料合成实现物理适应。受生物循环系统启发,利用流体重构材料界面,这一能力在现有机器人中尚属首创。我们实现了首个概念验证:在血管化复合机器人中通过“受体发生”(receptogenesis)按需构建传感器。通过协调前驱体的流体传输与外部局部紫外照射,驱动原位光聚合反应,将含光敏引发剂的前驱体转化为嵌入聚对苯乙烯-共-乙二醇(PETG)中的紫外线敏感聚吡咯固态分散体,形成可测电导变化的传感模态。新生成的传感器实时闭环控制仿蛾机器人翅膀拍打。本工作为血管化复合材料中原位硬件生成提供了概念验证,是迈向能响应环境线索的具身机器人的重要一步。

原文摘要 · Abstract (English)

Equipping robotic systems with the capacity to generate $\textit{ex novo}$ hardware during operation extends physical adaptability. Unlike modular systems that rely on discrete component integration pre- or post-deployment, we envision physical adaptation through continuous in-body development via hardware synthesis. Drawing inspiration from circulatory systems that redistribute mass and function in biological organisms, we utilize fluidics to restructure the material interface, a capability currently unmatched in robotics. Here, we realize this proof-of-concept hardware generation through a vascularized robotic composite designed for programmable material synthesis, demonstrated via receptogenesis - the on-demand construction of sensors. By coordinating the fluidic transport of precursors with external localized UV irradiation, we drove an $\textit{in situ}$ photopolymerization that chemically reconstructed the vasculature from the inside out. This reaction converted precursors with photolatent initiator into a solid dispersion of UV-sensitive polypyrrole in PETG, establishing a sensing modality validated by a characteristic decrease in electrical impedance. The newly synthesized sensor closed a local control loop in real time to regulate wing flapping in a moth-inspired robotic demonstrator. Our work is a proof-of-concept materials basis for $\textit{ex novo}$ hardware generation in a vascularized composite - a step towards situated robots adapting to environmental cues.

机器人自修复材料合成传感器

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