Tao Yebo, Yu Tingting, Jin Cheng, Yang Shangkun, Hu Zenan, Dickey Michael D, Yang Jiayi
College of Intelligent Manufacturing, Jiaxing Vocational & Technical College, Jiaxing Zhejiang 314036, China.
School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.
ACS Appl Mater Interfaces. 2025 Sep 24;17(38):54125-54138. doi: 10.1021/acsami.5c13858. Epub 2025 Sep 12.
Soft strain sensors are crucial for enabling humanoid robots to perform industrial, medical, and other human-related tasks. However, the limited internal space of humanoid robots exposes soft strain sensors to interference from line resistance, contact resistance, and alternating magnetic fields generated by motor actuator systems and power conversion circuits. To address this issue, inspired by biological neural signal systems, this work proposes an impedance-modulated soft strain sensor with high stability. The sensor combines a liquid metal (LM) resistor, a capacitor, and an inductor to form a passive band-stop filter, which is encapsulated in a soft elastomer. Tensile strain increases the resistance of the LM resistor, reducing the impedance of the sensor at resonance and converting the resistance signal into an impedance-modulated signal. Based on filter theory, the circuit structure of the sensor and the selection of the resistance, capacitance, and inductance components are analyzed in terms of stability, sensitivity, and measurement feasibility. By employing a series connection of resistance and inductance, the sensor achieves high impedance at resonance, effectively suppressing interference from line and contact resistance. Additionally, the frequency of the impedance-modulated sensor does not overlap with the frequency of electromagnetic interference (EMI) from the humanoid robot motor drivers, achieving immunity to EMI. Furthermore, a Field Programmable Gate Array-based signal acquisition system is constructed to measure the impedance-modulated signal. Finally, an application of this impedance-modulated sensor in humanoid robots and wearable devices is demonstrated.
柔性应变传感器对于使类人机器人能够执行工业、医疗及其他与人类相关的任务至关重要。然而，类人机器人有限的内部空间使柔性应变传感器容易受到电机执行系统和功率转换电路产生的线路电阻、接触电阻和交变磁场的干扰。为了解决这个问题，受生物神经信号系统的启发，这项工作提出了一种具有高稳定性的阻抗调制柔性应变传感器。该传感器将液态金属（LM）电阻器、电容器和电感器组合在一起，形成一个无源带阻滤波器，并封装在一种柔软的弹性体中。拉伸应变会增加LM电阻器的电阻，降低传感器在共振时的阻抗，并将电阻信号转换为阻抗调制信号。基于滤波器理论，从稳定性、灵敏度和测量可行性方面分析了传感器的电路结构以及电阻、电容和电感元件的选择。通过采用电阻和电感的串联连接，该传感器在共振时实现了高阻抗，有效抑制了线路和接触电阻的干扰。此外，阻抗调制传感器的频率与类人机器人电机驱动器产生的电磁干扰（EMI）频率不重叠，实现了对EMI的免疫。此外，构建了一个基于现场可编程门阵列的信号采集系统来测量阻抗调制信号。最后，展示了这种阻抗调制传感器在类人机器人和可穿戴设备中的应用。
