Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography

David Hardman , Thomas George Thuruthel , Fumiya Iida
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引用次数: 2

Abstract

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 multi-touch recognition - all using the same sensing material.

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基于数据驱动的电阻抗断层扫描的基于水凝胶的机器人皮肤的触觉感知
将功能性软材料与电阻抗断层扫描相结合是开发高分辨率连续传感软机器人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和分析模型面临着现实差距。为了应对这种情况,我们提出并演示了一种无模型叠加方法,该方法使用少量真实世界数据来开发由自修复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,获得12.1的平均分辨率 170以上mm mm圆形表皮。此外,我们还探讨了在连续15000次冲压过程中,这种分辨率是如何变化的,在这一过程中,损伤会不断传播。最后,我们展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。
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