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.

Robotic systems start to coexist around humans but cannot physically interact as humans do due to the absence of tactile sensitivity across their bodies. Various studies have developed a scalable tactile sensor to grant a body-scale robotic skin, yet many faced drawbacks arising from the rapidly increasing number of sensing elements or a limited sensibility to a wide range of touches. This article proposes a body-scale robotic skin composed of multimodal sensing modules and a multilayered fabric, simultaneously utilizing superresolution and tomographic transducing mechanisms. These mechanisms employ fewer sensing elements across a large area and complement each other in perceiving a wide range of stimuli humans can sense. Their measurements are processed to encode spatiotemporal properties of touch, which are decoded by a trained convolutional neural network to classify the touch modality, while their computational costs are minimized for on-device computation. The robotic skin was demonstrated on a commercial robotic arm and interpreted human touches for tactile communication, suggesting its capability as a body-scale robotic skin for further physical interaction.

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.

In this work, we propose a simple yet effective beam-forming technique for multiple-input multiple-output (MIMO) multi-pair two-way relay channels. Two key ingredients in our technique are adoption of signal space alignment (SSA) for transmit and receive beamforming and amplify-and-forward (AF) relay beamforming based on advanced zero-forcing (ZF) criterion. From the sum-rate analysis on MIMO multi-pair two-way relay channels, we show that the proposed method achieves full multiplexing gain and substantial beamforming gain in realistic multi-pair two-way relay scenario.