Ray Lala, Geißler Daniel, Zhou Bo, Lukowicz Paul, Greinke Berit
German Research Center for Artificial Intelligence (DFKI), Embedded Intelligence, Kaiserslautern, Germany.
RPTU Kaiserslautern-Landau, Kaiserslautern, Germany.
Sci Rep. 2024 Jul 29;14(1):17448. doi: 10.1038/s41598-024-67149-9.
In this work, we propose a novel single-end morphing capacitive sensing method for shape tracking, FxC, by combining Folding origami structures and Capacitive sensing to detect the morphing structural motions using state-of-the-art sensing circuits and deep learning. It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure. Different from other origami capacitors where the origami structures are used in adjusting the thickness of the dielectric layer of double-plate capacitors, FxC uses only a single conductive plate per channel, and the origami structure directly changes the geometry of the conductive plate. We examined the operation principle of morphing single-end capacitors through 3D geometry simulation combined with physics theoretical deduction, which deduced similar behavior as observed in experimentation. Then a software pipeline was developed to use the sensor signals to reconstruct the dynamic structural geometry through data-driven deep neural network regression of geometric primitives extracted from vision tracking. We created multiple folding patterns to validate our approach, based on folding patterns including Accordion, Chevron, Sunray and V-Fold patterns with different layouts of capacitive sensors using paper-based and textile-based materials. Experimentation results show that the geometry primitives predicted from the capacitive signals have a strong correlation with the visual ground truth with R-squared value of up to 95% and tracking error of 6.5 mm for patches. The simulation and machine learning constitute two-way information exchange between the sensing signals and structural geometry. By embedding part of the origami surface with morphing single-end capacitive sensors, FxC presents a unique solution that leverages both the mechanical properties of origami and sensing properties of capacitive sensing.
在这项工作中，我们提出了一种用于形状跟踪的新型单端变形电容传感方法——FxC，它通过结合折纸结构和电容传感，利用最先进的传感电路和深度学习来检测变形结构的运动。通过将折纸结构的区域嵌入导电材料作为单端电容传感贴片，观察到传感器信号随结构的运动而连贯变化。与其他折纸电容器不同，其他折纸电容器是将折纸结构用于调节双极板电容器介电层的厚度，而FxC每个通道仅使用一个导电板，并且折纸结构直接改变导电板的几何形状。我们通过三维几何模拟结合物理理论推导研究了变形单端电容器的工作原理，推导出了与实验中观察到的类似行为。然后开发了一个软件管道，通过从视觉跟踪中提取的几何基元的数据驱动深度神经网络回归，利用传感器信号来重建动态结构几何形状。我们创建了多种折叠图案来验证我们的方法，这些图案基于包括手风琴、人字形、太阳射线和V形折叠图案在内的多种折叠图案，使用纸质和纺织材料并采用不同的电容传感器布局。实验结果表明，从电容信号预测的几何基元与视觉地面真值具有很强的相关性，R平方值高达95%，贴片的跟踪误差为6.5毫米。模拟和机器学习构成了传感信号与结构几何形状之间的双向信息交换。通过将部分折纸表面嵌入变形单端电容传感器，FxC提供了一种独特的解决方案，利用了折纸的机械性能和电容传感的传感性能。
