Electrical Resistance Tomography (ERT) has emerged as a promising technology for large-area robotic skin due to its ability to reconstruct pressure distribution over extensive regions using a few sparsely distributed electrodes. Despite ERT's potential to reconstruct the external forces applied on 3D surfaces, the uneven distribution of spatial sensitivity leads to significant errors in identifying the physical quantities of contacts, inhibiting this technique from being an effective tactile sensor. To address this issue, this paper proposes a method to equalize the spatial sensitivity by modulating the conductivity of ERT sensors through topology optimization. In a simulation environment, the sensor's conductive domain was converted into a binary image and optimized to equalize spatial sensitivity and reduce disparities between low and highsensitivity areas. Additionally, we present a sensor fabrication method with a complex optimized conductive patch pattern from simulation by applying screen printing techniques. The effectiveness of the implemented spatial sensitivity equalization was validated by comparing it to a conventional ERT sensor in both simulations and real-world environments. The proposed sensitivity optimization method expands the use of ERT-based sensors for distributed tactile sensing in physical human-robot interaction scenarios.

Human employs different touch patterns to convey diverse social messages; for example, a stroke is an encouragement, whereas a hit is an offense. Various tactile sensors have been developed to grant an intuitive physical interaction with a robotic system, yet many encountered limitations in achieving broad sensibility or fabricating into a large skin. This paper presents a robotic skin with multimodal tactile sensing modules to achieve broad spatiotemporal sensibility with a few sensing elements. The multimodal module is composed of a microphone and a vented screw installed on a conductive sensory domain. A multilayered fabric with a textured surface covers the sensory domain and forms a piezoresistive structure. High and low temporal components of touch elicit a micro-vibration and a conductivity change on the skin, where both are measured with multimodal modules. The measurements are each processed with short-time Fourier transform (STFT) and electrical resistance tomography (ERT) to encode two spatiotemporal feature maps, which are classified into ten touch classes using a convolutional neural network. Due to a sensibility to both high and low temporal components of touch, the skin classifies touches with an accuracy of 97.0 %, whereas only 84.7 % and 90.6 % are achieved when one type of feature map is used. Also, the skin is robust and beneficial in power consumption and fabrication since the multimodal modules are not exposed to an external stimulus and are sparsely distributed.

The tactile and proprioceptive sensation increases human manipulability, and soft tissue compliance stabilizes the grasping function. However, it is challenging to transpose this system to the small confined space of soft robotic fingers due to the material properties and complex wiring entailed. Furthermore, soft robotic fingers also incorporate actuating components, making such a system more difficult to bring to fruition. Therefore, optimizing soft robotic finger structure for greater functionality and manufacturability would be a desirable innovation. In this study, we developed a soft somesthetic robotic finger based on the conductive working liquid and an origami structure. The proposed design comprises an origami structure, porous scaffolds, and a silicone-coated fabric outer layer. The robotic finger was filled with conductive liquid used for both somesthetic sensing and bending actuation simultaneously. The origami structure was fabricated by connecting printed circuit boards (PCBs) and a polyimide film, with electrodes embedded on each PCB to enable somesthetic sensing. The electrodes were used to inject currents and measure voltage, with the measured data then used to reconstruct the deformation map and hinge angle by means of electrical resistance tomography (ERT). The experimental results confirm that the robotic finger could acquire tactile and proprioceptive information in real-time.