Split-Type Magnetic Soft Tactile Sensor with 3D Force Decoupling

Optimized Magnetization Distribution in Body-Centered Cubic Lattice-Structured Magnetoelastomer for High-Performance 3D Force-Tactile Sensors

Flexible magnetic tactile sensors hold transformative potential in robotics and human-computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these limitations by amplifying the magnetoelastic effect through optimized magnetization direction distribution of the elastomer. A body-centered cubic lattice-structured magnetoelastomer featuring regular deformation under compression was fabricated via digital light processing (DLP) to validate this method. Finite element simulations and experimental analyses revealed that magnetizing the material under 60% compression strain optimized magnetization direction distribution, enhancing force-magnetic coupling. Integrating the magnetic elastomer with a hall sensor, the prepared tactile sensor demonstrated a low detection limit (1 mN), wide detection range (0.001-10 N), rapid response/recovery times (40 ms/50 ms), and durability (>1500 cycles). By using machine learning, the sensor enabled accurate 3D force prediction.

Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots

Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

Multi-Level High Entropy-Dissipative Structure Enables Efficient Self-Decoupling of Triple Signals

The theory of high entropy-dissipative structure is confined to high-entropy alloys and their oxide materials under harsh conditions, but it is very difficult to obtain high entropy-dissipative structure for smart sensors based on polymers and metal oxides under mild conditions. Moreover, multiple signal coupling effect heavily hinder the sensor applications, and current multimodal integrated devices can solve two signal-decoupling, but need very complicated process way. In this work, new synthesis concept is the first time to fabricate high entropy-dissipative conductive layer of smart sensors with triple-signal response and self-decoupling ability within poly-pyrrole/zinc oxide (PPy/ZnO) system. The sensor (SPZ20) amplifies pressure (17.54%/kPa) and gas (0.37%/ppm), reduces humidity (0.41%/% RH) and temperature (0.12%/°C) signals, simultaneously achieving the triple self-decoupling effect of pressure and gas in the complex temperature-humidity field because of the enlarged pressure-contact area, enhanced gas-responsive sites, altered vapor path and its own heat insulation function. Additionally, it inherits the strong robustness (500 rubbing, washing, and heating or freezing cycles) and endurance (10 000 photo-purification cycles) of traditional high-entropy materials for information transmission and smart alarms in emergencies or harsh environments. This work gives a new insight into the multiple-signal response and smart flexible electronic design from natural fibers.

Flexible Pressure, Humidity, and Temperature Sensors for Human Health Monitoring

The rapid advancements in artificial intelligence, micro-nano manufacturing, and flexible electronics technology have unleashed unprecedented innovation and opportunities for applying flexible sensors in healthcare, wearable devices, and human-computer interaction. The human body's tactile perception involves physical parameters such as pressure, temperature, and humidity, all of which play an essential role in maintaining human health. Inspired by the sensory function of human skin, many bionic sensors have been developed to simulate human skin's perception to various stimuli and are widely applied in health monitoring. Given the urgent requirements for sensing performance and integration of flexible sensors in the field of wearable devices and health monitoring, here is a timely overview of recent advances in pressure, humidity, temperature, and multi-functional sensors for human health monitoring. It covers the fundamental components of flexible sensors and categorizes them based on different response mechanisms, including resistive, capacitive, voltage, and other types. Specifically, the application of these flexible tactile sensors in the area of human health monitoring is highlighted. Based on this, an extended overview of recent advances in dual/triple-mode flexible sensors integrating pressure, humidity, and temperature tactile sensing is presented. Finally, the challenges and opportunities of flexible sensors are discussed.

Overview of 3D Printing Multimodal Flexible Sensors

With the growing demand for flexible sensing systems and precision engineering, there is an increasing need for sensors that can accurately measure and analyze multimode signals. 3D printing technology has emerged as a crucial tool in the development of multimodal flexible sensors due to its advantages in design flexibility and manufacturing complex structures. This paper provides a review of recent advancements in 3D printing technology within the field of multimode flexible sensors, with particular emphasis on the relevant working mechanisms involved in decoupling complex signals. First, the research status of 3D printed multimodal flexible sensors is discussed, including their responsiveness to different modal stimuli such as mechanics, temperature, and gas. Furthermore, it explores methods for decoupling multimodal signals through structural and material design, artificial intelligence, and other technologies. Finally, this paper summarizes current challenges such as limited material selection, difficulties in miniaturization integration, and crosstalk between multisignal outputs. It also looks forward to future research directions in this area.

Oriented Alginate-Poly(vinyl alcohol) Electrospun Nanofibers for Multimodal Sensing and Gesture Language Recognition

Flexible nanofiber sensors have gained substantial attention in extending application scenarios owing to their desirable lightweight, comfort, and breathability. Nevertheless, disorder and uneven dimension issues of nanofibers are the leading concerns in their multifunctional response, which often leads to erratic response signals as well as poor linearity. In this work, a high-performance oriented nanofiber film with a three-dimensional network consisting of alginate sodium, poly(vinyl alcohol), and poly(ethylene oxide) was successfully fabricated by a controllable directional electrospinning technique. The main properties of the nanofibers are capable of being regulated intentionally by varying the electrospinning temperature, collector rotation speed, and polymer concentrations. Based on the favorable structure orientation, the nanofiber film displays satisfied biodegradability and high mechanical strength (575.1 MPa). Being integrated with modified magnetic particles, the sensors not only display a fast response speed, high magnetic sensitivity, and exceptional recoverability in response to magnetic fields but also show favorable sensitivities and reliable long-term durability under mechanical excitations. As a wearable sensor, it can accurately perceive the physiological signals generated by the human body in real-time. Furthermore, with the assistance of a convolutional neural network model, a gesture language recognition system is developed by integrating multiple sensors to realize a high recognition accuracy (∼99.08%). This study provides a feasible strategy to manufacture high-performance multimodal sensors for wearable human-machine interaction applications.

Biomimetic Hydrodynamic Sensor with Whisker Array Architecture and Multidirectional Perception Ability

The rapid development of ocean exploration and underwater robot technology has put forward new requirements for underwater sensing methods, which can be used for hydrodynamic characteristics perception, underwater target tracking, and even underwater cluster communication. Here, inspired by the specialized undulated surface structure of the seal whisker and its ability to suppress vortex-induced vibration, a multidirectional hydrodynamic sensor based on biomimetic whisker array structure and magnetic 3D self-decoupling theory is introduced. The magnetic-based sensing method enables wireless connectivity between the magnetic functional structures and electronics, simplifying device design and endowing complete watertightness. The 3D self-decoupling capability enables the sensor, like a seal or other organisms, to perceive arbitrary whisker motions caused by the action of water flow without complex calibration and additional sensing units. The whisker sensor is capable of detecting a variety of hydrodynamic information, including the velocity (RMSE < 0.061 m s-1) and direction of the steady flow field, the frequency (error < 0.05 Hz) of the dynamic vortex wake, and the orientation (error < 7°) of the vortex wake source, demonstrating its extensive potential for underwater environmental perception and communication, especially in deep sea conditions.

All-Printed Finger-Inspired Tactile Sensor Array for Microscale Texture Detection and 3D Reconstruction

Electronic skins are expected to replicate a human-like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all-printed finger-inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual-column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro-textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self-calibration. Moreover, an innovative screen-printing technology incorporating multilayer printing and sacrificial-layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world.

Enhanced Magnetic Soft Robotics: Integrating Fiber Optics and 3D Printing for Rapid Actuation and Precision Sensing

Timely, accurate, and rapid grasping of dynamic change information in magnetic actuation soft robots is essential for advancing their evolution toward intelligent, integrated, and multifunctional systems. However, existing magnetic-actuation soft robots lack effective functions for integrating sensing and actuation. Herein, we demonstrate the integration of distributed fiber optics technology with advanced-programming 3D printing techniques. This integration provides our soft robots unique capabilities such as integrated sensing, precise shape reconstruction, controlled deformation, and sophisticated magnetic navigation. By utilizing an improved magneto-mechanical coupling model and an advanced inversion algorithm, we successfully achieved real-time reconstruction of complex structures, such as 'V', 'N', and 'M' shapes and gripper designs, with a notable response time of 34 ms. Additionally, our robots demonstrate proficiency in magnetic navigation and closed-loop deformation control, making them ideal for operation in confined or obscured environments. This work thus provides a transformative strategy to meet unmet demands in the rapidly growing field of soft robotics, especially in establishing the theoretical and technological foundation for constructing digitized robots.

Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.