一种基于接触的直观人力拖动教学的软协作机器人。

A Soft Collaborative Robot for Contact-based Intuitive Human Drag Teaching.

机构信息

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.

School of Mechanical Engineering and State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.

出版信息

Adv Sci (Weinh). 2024 Jun;11(24):e2308835. doi: 10.1002/advs.202308835. Epub 2024 Apr 22.

Abstract

Soft material-based robots, known for their safety and compliance, are expected to play an irreplaceable role in human-robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co-bot) with intuitive and easy programming by contact-based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra-low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self-sensing system, thus providing both precise actuation and dragging-aware collaboration. Hence, the Soft Co-bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co-bots exhibit a high potential for safe and intuitive human-robot collaboration in unstructured environments, promoting the immediate practical application of soft robots.

摘要

基于软物质的机器人以其安全性和顺应性而备受期待,有望在人机协作中发挥不可替代的作用。然而,由于软物质的超弹性和大滞后,其复杂的可编程性和较差的运动精度使得这一期望远未达到真正的工业应用。在这里,我们报道了一种具有直观、易于接触式拖动教学编程的软协作机器人(Soft Co-bot),其运动重复性也非常出色(<0.30%的体长),滞后性极低(<2.0%)。这种前所未有的能力是通过气动软机器人中的仿生拮抗设计实现的,其中电缆通过张力传感器穿入伺服电机,形成自感测系统,从而提供精确的驱动和拖动感知协作。因此,Soft Co-bot 可以先由人类拖动进行教学,然后自行精确地重复各种任务,例如电子产品组装、机床安装等。所提出的 Soft Co-bot 有望在非结构化环境中实现安全、直观的人机协作,推动软机器人的即时实际应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/041f/11200028/084f06505c7e/ADVS-11-2308835-g001.jpg

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