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Dual-Channel Tomographic Tactile Skin with Pneumatic Pressure Sensing for Improved Force Estimation
arXiv:2503.13036v2 Announce Type: replace
Abstract: Tactile skins based on Electrical Impedance Tomography (EIT) enable large-area contact localization with few electrodes, but suffer from nonuniform sensitivity that limits force estimation accuracy. This work introduces a dual-channel tactile skin that integrates an EIT layer with a pneumatic pressure layer and a calibration framework that leverages their complementary strengths. The EIT layer provides robust multi-contact localization, while the pneumatic pressure layer supplies a stable scalar measurement that serves as contact force estimation. A location-aware correction method is introduced, learning smooth spatial gain and offset fields from a single-session calibration, enabling spatially consistent multi-contact force estimation. The proposed system achieves accurate force estimation across diverse contact configurations, generalizes to varying indenter sizes, and preserves EIT's inherent advantages in multi-contact localization. By letting the pneumatic pressure layer handle the force estimation and using the EIT layer to determine where each contact occurs, the method avoids the need for large datasets, complicated calibration setups, and heavy machine-learning pipelines often required by previous EIT-only approaches. This dual-channel design provides a practical, scalable, and easy-to-calibrate solution for building large-area robotic skins.
Abstract: Tactile skins based on Electrical Impedance Tomography (EIT) enable large-area contact localization with few electrodes, but suffer from nonuniform sensitivity that limits force estimation accuracy. This work introduces a dual-channel tactile skin that integrates an EIT layer with a pneumatic pressure layer and a calibration framework that leverages their complementary strengths. The EIT layer provides robust multi-contact localization, while the pneumatic pressure layer supplies a stable scalar measurement that serves as contact force estimation. A location-aware correction method is introduced, learning smooth spatial gain and offset fields from a single-session calibration, enabling spatially consistent multi-contact force estimation. The proposed system achieves accurate force estimation across diverse contact configurations, generalizes to varying indenter sizes, and preserves EIT's inherent advantages in multi-contact localization. By letting the pneumatic pressure layer handle the force estimation and using the EIT layer to determine where each contact occurs, the method avoids the need for large datasets, complicated calibration setups, and heavy machine-learning pipelines often required by previous EIT-only approaches. This dual-channel design provides a practical, scalable, and easy-to-calibrate solution for building large-area robotic skins.
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