Skin-Inspired Highly Sensitive Tactile Sensors with Ultrahigh Resolution over a Broad Sensing Range

A deep neural network for tactile perception in open scenes

Tactile perception is important for the robots to understand their working environment. While in real-world applications, robots usually must face unexpected changes in external conditions, such as the re-installation of the robot end effector or the change of the installation location. Consequently, the collected tactile material data tend to vary to a certain extent, which brings great difficulties to the tactile perception. To handle this problem, different from the former studies of tactile perception in enclosed environments, this study focuses on the tactile material recognition task using robot electronic skin in open scenes. We construct a cross-batch tactile dataset to simulate open scenes and propose the multi-receptive field attention enhancement network (MRFE) to handle tactile material recognition. Compared with other machine learning algorithms, experiments show that the proposed method overcomes the problem of data drift caused by changes in posture, contact force, sliding velocities, exploratory motions, and assembly conditions.

Simultaneous Visualization of Dynamical and Static Tactile Perception Using Piezoelectric-Ultrasonic Bimodal Electronic Skin Based on In Situ Polarized PVDF-TrFE/2DBP Composites and the TFT Array

The key to realizing completed bionic tactile perception of human skin using electronic skin relies on simultaneously distinguishing dynamic and static stimuli and restoring their characteristic information, which is realized by integration of several individual sensors but remains certain limitations including large physical size and high energy consumption. In this study, a piezoelectric-ultrasonic bimodal electronic skin (PUVE) based on in situ polarized PVDF-TrFE/2DBP composites and a thin-film transistor (TFT) array is fabricated. The incorporation of 2DBP into the PVDF-TrFE film and the in situ polarization approach provide excellent piezoelectric and ultrasonic performances of PVDF-TrFE/2DBP composites. PUVE has an ultrahigh sensitivity of 3.2 mV kPa-1 over a wide pressure (0-310 kPa) range, with excellent spatial resolution (50 μm) and response time (40 ms). Meanwhile, the PUVE demonstrated outstanding repeatability and bending stability in 1500 cycles of cyclic pressure and 4000 cycles of 180° bending. The integrated piezoelectric and ultrasonic functions of PUVE can respond individually to dynamic and static tactile stimuli to ensure perceiving and decoupling of the dynamical and static mechanical signals with one single sensor. The PVDF-TrFE/2DBP composites is further integrated with the TFT array, realizing visualization function of contacting objects and restoring their characteristic information including the texture and location. Thus, the PUVE is expected to have a wide range of applications in intelligent robots and human prostheses.

Rise of Metal-Organic Frameworks: From Synthesis to E-Skin and Artificial Intelligence

Metal-organic frameworks (MOFs) have attained broad research attention in the areas of sensors, resistive memories, and optoelectronic synapses on the merits of their intriguing physical and chemical properties. In this review, recent progress on the synthesis of MOFs and their electronic applications is introduced and discussed. Initially, the crystal structures and properties of MOFs encompassing optical, electrical, and chemical properties are discussed in brief. Subsequently, advanced synthesis methods for MOFs are introduced, categorized into hydrothermal approach, microwave synthesis, mechanochemical synthesis, and electrochemical deposition. After that, the various roles of MOFs in widespread applications, including sensing, information storage, optoelectronic synapses, machine learning, and artificial intelligence, are discussed, highlighting their versatility and the innovative solutions they provide to long-standing challenges. Finally, an outlook on remaining challenges and a future perspective for MOFs are proposed.

Recent Advances in Self-Powered Tactile Sensing for Wearable Electronics

With the arrival of the Internet of Things era, the demand for tactile sensors continues to grow. However, traditional sensors mostly require an external power supply to meet real-time monitoring, which brings many drawbacks such as short service life, environmental pollution, and difficulty in replacement, which greatly limits their practical applications. Therefore, the development of a passive self-power supply of tactile sensors has become a research hotspot in academia and the industry. In this review, the development of self-powered tactile sensors in the past several years is introduced and discussed. First, the sensing principle of self-powered tactile sensors is introduced. After that, the main performance parameters of the tactile sensors are briefly discussed. Finally, the potential application prospects of the tactile sensors are discussed in detail.

Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception

Industrial robots are the main piece of equipment of intelligent manufacturing, and array-type tactile sensors are considered to be the core devices for their active sensing and understanding of the production environment. A great challenge for existing array-type tactile sensors is the wiring of sensing units in a limited area, the contradiction between a small number of sensing units and high resolution, and the deviation of the overall output pattern due to the difference in the performance of each sensing unit itself. Inspired by the human somatosensory processing hierarchy, we combine tactile sensors with artificial intelligence algorithms to simplify the sensor architecture while achieving tactile resolution capabilities far greater than the number of signal channels. The prepared 8-electrode carbon-based conductive network achieves high-precision identification of 32 regions with 97% classification accuracy assisted by a quadratic discriminant analysis algorithm. Notably, the output of the sensor remains unchanged after 13,000 cycles at 60 kPa, indicating its excellent durability performance. Moreover, the large-area skin-like continuous conductive network is simple to fabricate, cost-effective, and can be easily scaled up/down depending on the application. This work may address the increasing need for simple fabrication, rapid integration, and adaptable geometry tactile sensors for use in industrial robots.

Two-Stage Micropyramids Enhanced Flexible Piezoresistive Sensor for Health Monitoring and Human-Computer Interaction

High-performance flexible piezoresistive sensors are becoming increasingly essential in various novel applications such as health monitoring, soft robotics, and human-computer interaction. The evolution of the interfacial contact morphology determines the sensing properties of piezoresistive devices. The introduction of microstructures enriches the interfacial contact morphology and effectively boosts the sensitivity; however, the limited compressibility of conventional microstructures leads to rapid saturation of the sensitivity in the low-pressure range, which hinders their application. Herein, we present a flexible piezoresistive sensor featuring a two-stage micropyramid array structure, which effectively enhances the sensitivity while widening the sensing range. Owing to the synergistic enhancement effect resulting from the sequential contact of micropyramids of various heights, the devices demonstrate remarkable performance, including boosting sensitivity (30.8 kPa-1) over a wide sensing range (up to 200 kPa), a fast response/recovery time (75/50 ms), and an ultralong durability of 15,000 loading-unloading cycles. As a proof of concept, the sensor is applied to detect human physiological and motion signals, further demonstrating a real-time spatial pressure distribution sensing system and a game control system, showing great potential for applications in health monitoring and human-computer interaction.