Data-driven methods are attracting more and more attention in the field of electrical impedance tomography. Many learning-based tomographic algorithms have been presented and investigated in the past few years. However, few related studies pay attention to the symmetrical geometrical structure of tomographic sensors and the possible benefits it may bring to learning-based image reconstruction. Aiming to this, we propose the concept of electrical impedance maps, which can better reflect the nature of geometry of tomographic sensors and have similar properties to images. Then we design a fully convolutional network to build the relationship between electrical impedance maps and conductivity distribution images. The effectiveness and performance of our method is evaluated by both simulation and experimental datasets with different conductivity distribution patterns.

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Many robotic tasks require knowledge of the exact 3D robot geometry. However, this remains extremely challenging in soft robotics because of the infinite degrees of freedom of soft bodies deriving from their continuum characteristics. Previous studies have only achieved low proprioceptive geometry resolution (PGR), thus suffering from loss of geometric details (e.g. local deformation and surface information) and limited applicability. Here, we report an intelligent stretchable capacitive e-skin to endow soft robots with high PGR (=3,900) bodily awareness. We demonstrate that the proposed e-skin can finely capture a wide range of complex 3D deformations across the entire soft body through multi-position capacitance measurements. The e-skin signals can be directly translated to high-density point clouds portraying the complete geometry via a deep architecture based on transformer. This high PGR proprioception system providing millimeter-scale, local and global geometry reconstruction (2.322±0.687 mm error on a 20×20×200 mm soft manipulator) can assist in solving fundamental problems in soft robotics, such as precise closed-loop control and digital twin modelling.