Ori-Sense: origami capacitive sensing for soft robotic applications

TL;DR

Ori-Sense presents a compliant, origami-based capacitive sensor that converts torsional deformation into capacitance changes using an inverted Kresling geometry, enabling proprioception in soft robots. The device is fabricated via dissolvable-core molding to embed conductive TPU electrodes in silicone, forming a monolithic soft capacitor with low mechanical impedance. Mechanical and electrical characterizations, supported by FEM simulations, show ultra-low torque requirements, robust capacitive readout (up to ~30% change) tied to twist, and localized folding that protects the sensing interface. This approach offers a pathway to integrated, dual-mode, closed-loop soft sensing without significantly altering the host actuator’s compliance, with future work aimed at full electromechanical modeling and integration into pneumatic/origami actuators for autonomous control.

Abstract

This work introduces Ori-Sense, a compliant capacitive sensor inspired by the inverted Kresling origami pattern. The device translates torsional deformation into measurable capacitance changes, enabling proprioceptive feedback for soft robotic systems. Using dissolvable-core molding, we fabricated a monolithic silicone structure with embedded conductive TPU electrodes, forming an integrated soft capacitor. Mechanical characterization revealed low stiffness and minimal impedance, with torque values below 0.01 N mm for axial displacements between -15 mm and 15 mm, and up to 0.03 N mm at 30 degrees twist under compression. Finite-element simulations confirmed localized stresses along fold lines and validated the measured torque-rotation response. Electrical tests showed consistent capacitance modulation up to 30%, directly correlated with the twist angle, and maximal sensitivity of S_theta ~ 0.0067 pF/deg at 5 mm of axial deformation.

Figures (5)

• Figure 1: Concept of the Ori-Sense soft origami capacitor. The inverted Kresling structure translates torsional deformation into a measurable capacitance change, enabling proprioceptive sensing in soft robotic systems.
• Figure 2: Design of the origami structure: (a) Initial hexagon and point rotation by $\beta$. (b) Formation of faces $F_1$ and $F_2$. (c) Mirroring through a symmetry plane. (d) Circular array construction. (e) Linear array extension. (f) Addition of top and bottom lids. (g) Final assembled structure.
• Figure 3: Fabrication of the soft origami capacitive sensor: (a) Printing of BVOH core, addition of the capacitive plates and molding prepararton. (b) Mold assembly and silicone injection. (c) Core dissolution after curing. (d) 3D-printed core. (e) Conductive TPU plates. (f) Core with plates before molding. (g) Capacitive plates arrangement in silicone. (h) Final soft structure with integrated electrodes.
• Figure 4: Mechanical response of the inverted Kresling origami sensor: (a) Von Mises stress distribution at 30° of rotation. (b) Comparison between experimental and FEM torque–rotation curves at zero axial strain, together with extreme cases. (c) Torque–rotation curves for varying axial displacements. (d) Peak torques for the structure. (e) Axial force versus rotation angle for different axial offsets. (f) Peak axial force and torque, indicating symmetric trends between tensile and compressive deformation.
• Figure 5: Electrical characterization of the soft origami capacitive sensor. (a) Normalized sensor output during repeated torsional cycles (0°–30°) for axial displacements of -15;-10;-5;0;5;10;15. (b) Normalized signal for the case of -15mm axial displacement. (c) Normalized signal for -10mm. (d) Normalized signal for -5mm. (e) Normalized signal for 0mm. (f) Normalized signal for 10mm. (g) Normalized signal for 15mm.