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一种用于毫米级自感知软硬混合机器人的制造策略。

A fabrication strategy for millimeter-scale, self-sensing soft-rigid hybrid robots.

作者信息

Lee Hun Chan, Elder Nash, Leal Matthew, Stantial Sarah, Vergara Martinez Elenis, Jos Sneha, Cho Hyunje, Russo Sheila

机构信息

Department of Mechanical Engineering, Boston University, Boston, MA, USA.

Department of Biomedical Engineering, Boston University, Boston, MA, USA.

出版信息

Nat Commun. 2024 Sep 30;15(1):8456. doi: 10.1038/s41467-024-51137-8.

Abstract

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks.

摘要

软机器人通常需要手动组装诸如致动器、传感器和控制器等核心硬件组件。这增加了制造时间并降低了一致性,尤其是在小型软机器人中。我们提出了一种用于毫米级软硬混合机器人的可扩展单片制造方法,简化了核心硬件组件的集成。通过注入离子流体驱动的基于软可折叠聚四氟乙烯薄膜的致动器来提供驱动。通过集成由刚柔材料组成的机械控制器来编码所需的运动。通过检测机器人身体上的电阻变化,使用离子电阻传感器可以自感知机器人的运动。我们通过制造三个不同的软硬混合机器人模块展示了我们的方法,每个模块都具有独特的自由度:平移、弯曲和旋转平移运动。这些模块连接起来形成一个具有实时形状感知能力的软硬混合连续体机器人。我们通过执行物体拾取和放置、针引导和组织穿刺以及光纤引导任务来展示机器人的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60c3/11442515/ef5a2aba8b82/41467_2024_51137_Fig1_HTML.jpg

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