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:软机器人学因其柔性和可塑性而适用于各种领域。为了开发未来智能的软机器人,需要软传感器,它可以捕捉带有近乎无限自由度的变形。软传感器网络可以解决这个问题,但是测量整个传感器网络中的所有传感器值需要过多的布线和复杂的制造工艺,可能会影响机器人性能。我们通过开发一种基于电阻器网络反问题求解算法的非侵入式测量技术,避免了这些挑战,并将此算法实现在软抗性应变传感器网络上。我们的算法通过迭代计算电阻值来解决应用边界电压和电流响应的反问题,并分析算法的重构误差作为网络尺寸和测量误差的函数。我们进一步开发了电子组件装置,在由软导电硅制成的可伸缩抗性应变传感器网络上实现了我们的算法,并展示了测量网络对不同变形模式的响应能力。我们的工作为解决软传感器中测量许多传感器值的挑战开辟了新的道路,并可用于软性机器人传感器系统。