A Multimodal Hydrogel Soft-Robotic Sensor for Multi-Functional Perception - Cheng, Yu; Zhang, Runzhi; Zhu, Wenpei; Zhong, Hua; Liu, Sicong; Yi, Juan; Shao, Liyang; Wang, Wenping; Lam, James; Wang, Zheng (Frontiers in Robotics and AI, 2021)
Soft robots, with their unique and outstanding capabilities of environmental conformation, natural sealing against elements, as well as being insensitive to magnetic/electrical effects, are ideal candidates for extreme environment applications. However, sensing for soft robots in such harsh conditions would still be challenging, especially under large temperature change and complex, large deformations. Existing soft sensing approaches using liquid-metal medium compromise between large deformation and environmental robustness, limiting their real-world applicability. In this work, we propose a multimodal solid-state soft sensor using hydrogel and silicone. By exploiting the conductance and transparency of hydrogel, we could deploy both optical and resistive sensing in one sensing component. This novel combination enables us to benefit from the in-situ measurement discrepancies between the optical and electrical signal, to extract multifunctional measurements. Following this approach, prototype solid-state soft sensors were designed and fabricated, a dedicated neural network was built to extract the sensory information. Stretching and twisting were measured using the same sensor even at large deformations. In addition, exploiting the distinctive responses against temperature change, we could estimate environmental temperatures simultaneously. Results are promising for the proposed solid-state multimodal approach of soft sensors for multifunctional perception under extreme conditions.