Suppr超能文献

连接刚与柔:机械超材料实现软机器人中的刚性扭矩传递。

Bridging hard and soft: Mechanical metamaterials enable rigid torque transmission in soft robots.

作者信息

Carton Molly, Kowalewski Jakub F, Guo Jiani, Alpert Jacob F, Garg Aman, Revier Daniel, Lipton Jeffrey Ian

机构信息

Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

Department of Mechanical Engineering, University of Maryland, 4289 Campus Dr., College Park, MD 20742, USA.

出版信息

Sci Robot. 2025 Mar 19;10(100):eads0548. doi: 10.1126/scirobotics.ads0548.

Abstract

Torque and continuous rotation are fundamental methods of actuation and manipulation in rigid robots. Soft robot arms use soft materials and structures to mimic the passive compliance of biological arms that bend and extend. This use of compliance prevents soft arms from continuously transmitting and exerting torques to interact with their environment. Here, we show how relying on patterning structures instead of inherent material properties allows soft robotic arms to remain compliant while continuously transmitting torque to their environment. We demonstrate a soft robotic arm made from a pair of mechanical metamaterials that act as compliant constant-velocity joints. The joints are up to 52 times stiffer in torsion than bending and can bend up to 45°. This robot arm continuously transmits torque while remaining flexible in all other directions. The arm's mechanical design achieves high motion repeatability (0.4 millimeters and 0.1°) when tracking trajectories. We then trained a neural network to learn the inverse kinematics, enabling us to program the arm to complete tasks that are challenging for existing soft robots, such as installing light bulbs, fastening bolts, and turning valves. The arm's passive compliance makes it safe around humans and provides a source of mechanical intelligence, enabling it to adapt to misalignment when manipulating objects. This work will bridge the gap between hard and soft robotics with applications in human assistance, warehouse automation, and extreme environments.

摘要

扭矩和连续旋转是刚性机器人中驱动和操作的基本方法。软体机器人手臂使用柔软的材料和结构来模仿生物手臂弯曲和伸展时的被动柔顺性。这种柔顺性的使用使得软体手臂无法持续传递和施加扭矩来与周围环境相互作用。在此,我们展示了如何依靠图案化结构而非材料固有特性,使软体机器人手臂在向周围环境持续传递扭矩的同时保持柔顺性。我们展示了一种由一对机械超材料制成的软体机器人手臂,它们充当柔顺的等速关节。这些关节在扭转时的刚度比弯曲时高52倍,并且可以弯曲达45°。该机器人手臂在持续传递扭矩的同时,在所有其他方向上仍保持灵活。在跟踪轨迹时,该手臂的机械设计实现了高运动重复性(0.4毫米和0.1°)。然后,我们训练了一个神经网络来学习逆运动学,使我们能够对该手臂进行编程,以完成对现有软体机器人具有挑战性的任务,例如安装灯泡、拧紧螺栓和转动阀门。该手臂的被动柔顺性使其在人类周围很安全,并提供了一种机械智能来源,使其在操纵物体时能够适应不对准情况。这项工作将弥合硬机器人和软机器人之间的差距,在人类辅助、仓库自动化和极端环境等领域具有应用价值。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验