Page 390 - Control Theory in Biomedical Engineering

356 Control theory in biomedical engineering

application in the medical industry. One of the biggest challenges for soft and
flexible tubular MIS equipment is the load bearing capacity and the ability to
transform from a flexible compliant device into a stiff and rigid device when
required. Currently available methods of achieving tunable stiffness are
either not suited for fast-changing applications or require high temperature
and pressure applications, which may pose a safety risk when being used
inside human bodies. Furthermore, the change in stiffness happens in bulk
across the device, making it difficult to selectively stiffen certain regions
independently. Instead, we theorize and design a completely novel means
of achieving tunable stiffness using auxetic materials.
We first tested the method of jamming using auxetic materials to achieve
a change in stiffness. We also created folding patterns that exhibit stiffness
tunability using kirigami and origami methods. These new methods are
compatible with our existing tendon-driven mechanism and provide an
integrated actuation and stiffness modulation method. These methods are
safe and suitable for biomedical applications where the stiffness can be iso-
lated to specific regions.

References

Balasubramanian, A., Standish, M., Bettinger, C.J., 2014. Microfluidic thermally activated
materials for rapid control of macroscopic compliance. Adv. Funct. Mater. https://
doi.org/10.1002/adfm.201304037.
Behbahani, S.B., Tan, X., 2017. Design and dynamic modeling of electrorheological fluid-
based variable-stiffness fin for robotic fish. Smart Mater. Struct. https://doi.org/
10.1088/1361-665x/aa7238.
Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R., et al., 2010.
Universal robotic gripper based on the jamming of granular material. Proc. Natl. Acad.
Sci. U. S. A. https://doi.org/10.1073/pnas.1003250107.
Carlson, J.D., Jolly, M.R., 2000. MR fluid, foam and elastomer devices. Mechatronics.
https://doi.org/10.1016/S0957-4158(99)00064-1.
Chen, L., Gong, X.L., Li, W.H., 2007. Microstructures and viscoelastic properties of aniso-
tropic magnetorheological elastomers. Smart Mater. Struct. https://doi.org/
10.1088/0964-1726/16/6/069.
Chen, J., Lau, H.Y.K., Xu, W., Ren, H., 2016. Towards transferring skills to flexible surgical
robots with programming by demonstration and reinforcement learning. In: Proceedings
of the 8th International Conference on Advanced Computational Intelligence, ICACI
2016. https://doi.org/10.1109/ICACI.2016.7449855.
Cheng, N.G., Gopinath, A., Wang, L., Iagnemma, K., Hosoi, A.E., 2014. Thermally tun-
able, self-healing composites for soft robotic applications. Macromol. Mater. Eng.
https://doi.org/10.1002/mame.201400017.
Clark, W.W., Brigham, J.C., Mo, C., Joshi, S., 2010. Modeling of a high-deformation shape
memory polymer locking link. In: Industrial and Commercial Applications of Smart
Structures Technologies 2010. https://doi.org/10.1117/12.847987.