Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography
Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography
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DOI：
10.1016/j.mtelec.2023.100032
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发表时间：
2023-06-01
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影响因子：
--
通讯作者：
Iida, Fumiya
中科院分区：
文献类型：
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作者：
Hardman, David;Thuruthel, Thomas George;Iida, Fumiya
将功能软材料与电阻抗层析成像相结合是开发高分辨率连续介质传感机器人软皮肤的一种很有前途的方法。然而，从表面电极测量重建触觉刺激是一个具有挑战性的不适定建模问题，FEM和分析模型面临现实差距。为了解决这个问题，我们提出并展示了一种无模型叠加方法，该方法使用少量的现实世界数据来开发由自愈离子导电水凝胶制成的柔软机器人皮肤的变形图，其特性受温度、湿度和损伤的影响。我们展示了这种方法如何在小数据集上优于传统的神经网络，在170毫米的圆形皮肤上获得12.1毫米的平均分辨率。此外，我们还探讨了该分辨率在15,000台连续印刷机上的变化情况，在此过程中，损伤会不断传播。最后，我们演示了功能性机器人皮肤的应用：损伤检测/定位，环境监测和多点触摸识别-所有这些都使用相同的传感材料。
Combining functional soft materials with electrical impedance tomography is a promising method for developing continuum sensorized soft robotic skins with high resolutions. However, reconstructing the tactile stimuli from surface electrode measurements is a challenging ill-posed modelling problem, with FEM and analytic models facing a reality gap. To counter this, we propose and demonstrate a model -free superposition method which uses small amounts of real-world data to develop deformation maps of a soft robotic skin made from a self-healing ionically conductive hydrogel, the properties of which are affected by temperature, humidity, and damage. We demonstrate how this method outperforms a traditional neural network for small datasets, obtaining an average resolution of 12.1 mm over a 170 mm circular skin. Additionally, we explore how this resolution varies over a series of 15,000 consecutive presses, during which damages are continuously propagated. Finally, we demonstrate applications for functional robotic skins: damage detection/localization, environmental monitoring, and multitouch recognition - all using the same sensing material.