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.

This paper presents MelumiTac, a vision-based tactile (ViTAC) sensor enhanced with mechanoluminescent (ML) materials that emit green light under dynamic tactile stimuli. The integration of an ML elastomer generates self-illumination in response to dynamic tactile stimuli, enabling direct visualization of both dynamic tactile events and nociceptive responses while simultaneously tracking deformation in real-time. Experimental evaluations involving cyclic loading, in-plane motion, and piercing reveal a strong correlation between ML emission, stress rate, and localized deformation, thereby validating its multi-modal tactile sensing capabilities. Additionally, frame-by-frame analysis offers rich insights into the contact dynamics during physical interactions. These improvements, implemented within a small form factor of conventional ViTac sensor, render the approach highly accessible. Thus, we expect that the proposed solution will offer practical and unique advantages to engineers developing and applying vision-based multi-modal tactile sensors.

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.

Soft tactile sensors can enable robots to grasp objects easily and stably by simultaneously providing tactile data and mechanical compliance to robotic hands. If there are low-cost and easy-to-build robotic hands equipped with soft tactile sensors, they would be highly accessible and facilitate many robotics projects. To this end, we propose an accessible robot hand capable of tactile sensing, which can be produced through digital fabrication. We made the robot hand using commercial servo motors as well as components 3D printed from PETG, TPU, and conductive TPU. These materials allow the robot hand to have a soft, durable, and even functional structure. Specifically, the soft fingertip was crafted from TPU and conductive TPU, and their mechanical and electrical properties enable easy implementation of tactile sensing capabilities, such as force and capacitive touch, simply by adding off-the-shelf sensors (air-pressure and capacitance). The proposed robot hand could effectively sense interaction forces and proximity to conductive objects, and its utilization in various tasks was also demonstrated successfully.

Although many soft robotic skins have been introduced, their use has been hindered due to practical limitations such as difficulties in manufacturing, poor accessibility, and cost inefficiency. To solve this, we present a low-cost, easy-to-build soft robotic skin utilizing air-pressure sensors and 3D-printed pads. In our approach, we utilized digital fabrication and ROS to facilitate the creation and use of the robotic skin. The skin pad was fabricated by printing thermoplastic urethane (TPU) and post-processed with an organic solvent to secure air-tightness. Each pad consists of a TPU shell and infill, so the internal air pressure changes in response to tactile stimuli such as force and vibration. The internal pressure is measured and processed by a microcontroller and transmitted to the PC via a serial bus. We conducted experiments to investigate the characteristics of the skin pads, and the results showed that the developed robotic skins are capable of perceiving interaction force and dynamic stimuli. Finally, we developed the dedicated soft robotic skins for our custom robot designed in-house, and demonstrated safe and intuitive physical human-robot 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.

A lightweight and compliant manipulator design has been considered crucial in safe physical human–robot interaction. Remote actuation relocating the massive parts to the robot base and transmitting power to the distal joint minimizes the actuator inertia and provides series elasticity to the actuator. Rolling diaphragm hydrostatic transmission (RDHT), one of the remote actuation, has recently been studied in physically interacting robots, which can tackle the remaining issues in hydraulic actuation, such as low backdrivability and fluid leakage. However, existing RDHTs are challenging to achieve the desired safety and control performance simultaneously due to their fixed stiffness. This article presents a stiffness-switchable hydrostatic transmission (SwHST) consisting of an RDHT and valve-controlled pneumatic springs. The SwHST has a wide stiffness range of 15–290 N$\cdot$m/rad and a fast response in stiffness transition of less than 50 ms without any complex stiffness tuning mechanism. It is one of the most efficient transmissions in remote actuation and stiffness adjustment. Its static friction is less than 0.4% of full-range torque, and the stiffness-switching module consumes only 6 W of power when valves are open. The dynamic characteristics of the SwHST are experimentally scrutinized under various operational conditions. Safety performance is verified in unconstrained and constrained collision tests, demonstrating that the SwHST can effectively mitigate the clamping force of more than 50% for both the tests. Control performance is evaluated on position tracking tests. We foresee the proposed SwHST being utilized in human–robot collaboration without jeopardizing control performance through a rapid and efficient stiffness-switching mechanism.

Electrical resistance tomography (ERT) is an inferential imaging technique that has been utilized to develop large-scale robotic skin due to its scalable and practical properties. The performance of ERT-based sensors has been improved by optimizing the electrode arrangement, but it has relied on a heuristic design due to an absence of quantitative studies on the effects of the electrode arrangement. This article introduces a novel design method to optimize an electrode arrangement for ERT-based robotic skins. The method is based on a neural-gas network, and it finds an optimal design that maximizes the minimum electric current density. The optimal design was comprehensively evaluated using the ERT forward model, and the result revealed that the optimal design achieved the best intrinsic properties in terms of ill-posedness, sensitivity, and spatial discriminability. For validation, we conducted an indentation experiment on ERT-based robotic skin with the optimal design. Although only 30 electrodes were used to cover the 700 cm2sensing area, physical contacts could be localized with an error of 6.6 ± 3.5 mm, and the two-point resolution was adequate for daily tasks. Finally, the developed robotic skin was integrated with a commercial robot arm, and its use in physical human–robot interaction was demonstrated.

A human can socially interact in a non-verbal manner by understanding the intention behind a tactile stimulus. Patting on one’s back is one of tactile communications, which is considered as a sign of encouragement in most cultures. The majority of such tactile communication is carried out by a dynamic tactile on large passive body parts and differently interpreted by how and where on the body is touched. Thus any robotic system that physically interacts with a human requires a dynamic tactile sensor for further social interaction. This paper presents a large dynamic tactile sensor that could cover a robot’s passive body parts using a few sparsely distributed microphones to cover a large area in an efficient manner. A porous structured mesh, neoprene, and loop fabric are used to form a sensor’s skin that could well generate and transfer a signal to distributed microphones when a touch is introduced. TDOA source localisation algorithms are implemented to find the touch point locating in between the distributed microphones, and a simple convolutional neural network is trained to classify a type of the touch. A localising performance is qualitatively achieved in a testbed of the sensor and applied to a mannequin’s back to show the applicability, which classified a touch into six classes with an accuracy of 88 %.

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.

Hydrostatic transmission has shown promising results for enabling the manipulator to achieve low effective inertia, high stiffness, and high torque density. However, the incompressibility of fluid causes the lack of compliance, so that it could not provide intrinsic safety. Thus, it would be advantageous to introduce series compliance on the hydrostatic manipulator for adjusting stiffness depending on the situation. Here, we developed a safe and high-performance hydrostatic actuator based on the switchable stiffness mechanism implemented with an air spring and solenoid valve. The hydrostatic transmission is implemented with rolling diaphragms to attain zero fluid leakage and low seal friction. Air spring is serially connected to hydraulic lines for achieving compliance. Its modes (i.e., stiff and compliant modes) can be rapidly switched by modulating water flow via the solenoid valve. Block stiffness experiment shows that the stiffness of stiff mode is 9.63 times stiffer than one of compliant mode at 100 kPa. We experimentally demonstrated that compliant mode could mitigate the impact force to the level that a tangerine would not be crushed. The stiffness was switched within 12 ms; hence it is fast enough to be used for feedback application with vision or tactile sensors. As a result, the developed actuator can ensure safety without sacrificing the dynamic performance, owing to the simple and rapidly switchable stiffness mechanism.

Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.

Electrical resistance tomography (ERT) has previously been utilized to develop a large-scale tactile sensor because this approach enables the estimation of the conductivity distribution among the electrodes based on a known physical model. Such a sensor made with a stretchable material can conform to a curved surface. However, this sensor cannot fully cover a cylindrical surface because in such a configuration, the edges of the sensor must meet each other. The electrode configuration becomes irregular in this edge region, which may degrade the sensor performance. In this paper, we introduce an ERT-based robotic skin with evenly and sparsely distributed electrodes. For implementation, we sprayed a carbon nanotube (CNT)-dispersed solution to form a conductive sensing domain on a cylindrical surface. The electrodes were firmly embedded in the surface so that the wires were not exposed to the outside. The sensor output images were estimated using a deep neural network (DNN), which was trained with noisy simulation data. An indentation experiment revealed that the localization error of the sensor was 5.2 ± 3.3 mm, which is remarkable performance with only 30 electrodes. A frame rate of up to 120 Hz could be achieved with a sensing domain area of 90 cm2. The proposed approach simplifies the fabrication of 3D-shaped sensors, allowing them to be easily applied to existing robot arms in a seamless and robust manner.

Robots operating in unstructured environments would benefit from soft whole-body tactile sensors, but implementing such systems typically requires complex electrical wiring to a large number of sensing elements. The reconstruction method called electrical resistance tomography (ERT) has shown promising results (good coverage, manufacturability, and robustness) using electrodes located only along the boundary of the sensing region. However, relatively poor spatial resolution in the sensor's central region is a major drawback of the ERT approach. This paper introduces a new scheme of internal array electrodes to improve spatial resolution. We also systematically derive the optimal pairwise current injection patterns from a mathematical formulation of the ERT system. By highlighting the importance of each electrode pair, this approach enabled us to reduce the number of current injection patterns. Simulation of the standard and proposed sensor designs revealed that the internal array electrodes greatly improve distinguishability in the central region. For validation, a fabric-based soft tactile sensor made of multiple conductive fabrics was developed, including electronics that enable sampling at 200 Hz. During a 225-point localization test conducted without sensor-specific calibration, the constructed sensor showed average localization errors of 2.85 cm ± 1.02 cm. This result is notable because only 16 point electrodes were used to achieve this performance.

Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell's equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.

Summary form only given. Over the past few years, the development of technology has allowed new advances in sensor networks that monitor the physical world. The data streams produced by sensor networks have different characteristics from the data of traditional data processing, thus requiring new paradigms of data processing systems. Recently, a lot of research has been reported on data stream processing systems. However, there are many underlying assumptions about these systems that have not been explicitly specified. In this paper, our attempt is to provide a general model and architecture for data stream processing in sensor networks. This can serve as a reference architecture to better understand and categorize research in this area.

In this paper, we propose a protocol, called asynchronous data delivery protocol (AD/sup 2/P), in multi-hop wireless networks for processing time-uncritical data which needs to be stored in or retrieved from a database in the Internet using the existing TCP-based applications.