Stiffness-tunable and self-sensing integrated soft machines based on 4D printed conductive shape memory composites
Luquan Ren, Qian Wu, Qingping Liu, Pingting Hao, Jinghao Tang, Jianyang Li, Yulin He, Kunyang Wang, Lei Ren, Xueli Zhou, Bingqian Li, Huili Liu
- 发表年份
- 2023
- 引用次数
- 25
摘要
Through the synergy of nervous system and the self-regulation of muscle stiffness, living organisms are capable of quickly adjusting movements and actively adapting to dynamic environments. Likewise, the stiffness-changing and self-sensing functionalities are critical to empower soft robots with adjustable load capacity and agile movement. This work presents a paradigm for the design and fabrication of soft actuators with stiffness tunability and intrinsic self-sensing feedback through 4D printing method. The integration of 4D printed conductive composite into soft actuator body allows for stiffness adjustment within three orders of magnitude and ensures real-time stiffness-sensing, bending-sensing and pressure-sensing feedback. By constructing the theoretical deformation models and decoupling the resistance signals by machine learning methods, information on the stiffness, deformation and pressure of the material at different temperatures can be obtained. Cardiac-mimicking actuator and active therapeutic insoles are fabricated as proof-of-concept demonstrations, among which the active therapeutic insoles can perceive plantar pressure changes and conduct pressure-releasing actuation to ease the pain of patient at walk. It demonstrates the potential of this design approach for versatile applications such as medical assistive devices, artificial muscles, soft robotics, etc.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991