Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformation with nearly infinite degree-of-freedom are necessary. Soft sensor networks can address this problem, however, measuring all sensor values throughout the body requires excessive wiring and complex fabrication that may hinder robot performance. We circumvent these challenges by developing a non-invasive measurement technique, which is based on an algorithm that solves the inverse problem of resistor network, and implement this algorithm on a soft resistive, strain sensor network. Our algorithm works by iteratively computing the resistor values based on the applied boundary voltage and current responses, and we analyze the reconstruction error of the algorithm as a function of network size and measurement error. We further develop electronics setup to implement our algorithm on a stretchable resistive strain sensor network made of soft conductive silicone, and show the response of the measured network to different deformation modes. Our work opens a new path to address the challenge of measuring many sensor values in soft sensors, and could be applied to soft robotic sensor systems.

翻译：软体机器人因其柔软、顺应性材料的适应性而适用于多个领域。为开发未来的智能软体机器人，需要能够捕捉近乎无限自由度的变形的软体传感器。软体传感器网络可以解决这一问题，但测量全身所有传感器数值需要过多布线和复杂制造，可能影响机器人性能。我们通过开发一种非侵入式测量技术来规避这些挑战，该技术基于求解电阻网络逆问题的算法，并将其实现于电阻应变型软体传感器网络。该算法通过基于施加的边界电压和电流响应迭代计算电阻值，并分析了算法重构误差随网络规模和测量误差的变化规律。我们进一步开发了电子电路系统，将算法应用于由柔性导电硅胶制成的可拉伸电阻应变传感器网络，并展示了网络对不同变形模式的响应。本工作为解决软体传感器中众多传感器数值测量的难题开辟了新途径，并有望应用于软体机器人传感系统。