AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.

Design, Fabrication, and Characterization of Inkjet-Printed Organic Piezoresistive Tactile Sensor on Flexible Substrate

In this paper, we propose a novel tactile sensor with a "fingerprint" design, named due to its spiral shape and dimensions of 3.80 mm × 3.80 mm. The sensor is duplicated in a four-by-four array containing 16 tactile sensors to form a "SkinCell" pad of approximately 45 mm by 29 mm. The SkinCell was fabricated using a custom-built microfabrication platform called the NeXus which contains additive deposition tools and several robotic systems. We used the NeXus' six-degrees-of-freedom robotic platform with two different inkjet printers to deposit a conductive silver ink sensor electrode as well as the organic piezoresistive polymer PEDOT:PSS-Poly (3,4-ethylene dioxythiophene)-poly(styrene sulfonate) of our tactile sensor. Printing deposition profiles of 100-micron- and 250-micron-thick layers were measured using microscopy. The resulting structure was sintered in an oven and laminated. The lamination consisted of two different sensor sheets placed back-to-back to create a half-Wheatstone-bridge configuration, doubling the sensitivity and accomplishing temperature compensation. The resulting sensor array was then sandwiched between two layers of silicone elastomer that had protrusions and inner cavities to concentrate stresses and strains and increase the detection resolution. Furthermore, the tactile sensor was characterized under static and dynamic force loading. Over 180,000 cycles of indentation were conducted to establish its durability and repeatability. The results demonstrate that the SkinCell has an average spatial resolution of 0.827 mm, an average sensitivity of 0.328 mΩ/Ω/N, expressed as the change in resistance per force in Newtons, an average sensitivity of 1.795 µV/N at a loading pressure of 2.365 PSI, and a dynamic response time constant of 63 ms which make it suitable for both large area skins and fingertip human-robot interaction applications.

Bionic ordered structured hydrogels: structure types, design strategies, optimization mechanism of mechanical properties and applications

Natural organisms, such as lobsters, lotus, and humans, exhibit exceptional mechanical properties due to their ordered structures. However, traditional hydrogels have limitations in their mechanical and physical properties due to their disordered molecular structures when compared with natural organisms. Therefore, inspired by nature and the properties of hydrogels similar to those of biological soft tissues, researchers are increasingly focusing on how to investigate bionic ordered structured hydrogels and render them as bioengineering soft materials with unique mechanical properties. In this paper, we systematically introduce the various structure types, design strategies, and optimization mechanisms used to enhance the strength, toughness, and anti-fatigue properties of bionic ordered structured hydrogels in recent years. We further review the potential applications of bionic ordered structured hydrogels in various fields, including sensors, bioremediation materials, actuators, and impact-resistant materials. Finally, we summarize the challenges and future development prospects of bionic ordered structured hydrogels in preparation and applications.

PhotoElasticFinger: Robot Tactile Fingertip Based on Photoelastic Effect

The sense of touch is fundamental for a one-to-one mapping between the environment and a robot that physically interacts with the environment. Herein, we describe a tactile fingertip design that can robustly detect interaction forces given data collected from a camera. This design is based on the photoelastic effect observed in silicone matter. Under the force applied to the silicone rubber, owing to the stress-induced birefringence, the light propagating within the silicone rubber is subjected to the angular phase shift, where the latter is proportional to the increase in the image brightness in the camera frames. We present the calibration and test results of the photoelastic sensor design on a bench using a robot arm and with a certified industrial force torque sensor. We also discuss the applications of this sensor design and its potential relationship with human mechano-transduction receptors. We achieved a force sensing range of up to 8 N with a force resolution of around 0.5 N. The photoelastic tactile fingertip is suitable for robot grasping and might lead to further progress in robust tactile sensing.

Multifunctional Electronic Skins Enable Robots to Safely and Dexterously Interact with Human

Human-robot collaboration is playing more and more important roles in current deployments of robotic systems in our lives. Haptic perception and intelligent control are essential to ensure safety and efficiency of human-robot interaction. However, existing robotic sensory and control systems are deficient in terms of performance issues, complexity, and cost. Here, the authors report a multifunctional electronic skin (e-skin) incorporating multiple perceptions with intelligent robotic control, by which robots can safely and dexterously interact with humans. The e-skin with a simple and cost-effective sensory structure has multimodal perceptions of proximity, temperature, contact force, and contact position with broad measuring range, high sensitivity, and fast response. The e-skin is applied onto robots to accomplish obstacle avoidance, safe and dexterous human-robot interaction, smart teaching, and playing Tai-Chi, which demonstrate a broad range of applications for intelligent robots equipped with e-skins.

A Multi-Modal Sensor Array for Human-Robot Interaction and Confined Spaces Exploration Using Continuum Robots

Safe human-robot interaction requires robots endowed with perception. This paper presents the design of a multi-modal sensory array for continuum robots, targeting operation in semi-structured confined spaces with human users. Active safety measures are enabled via sensory arrays capable of simultaneous sensing of proximity, contact, and force. Proximity sensing is achieved using time-of-flight sensors, while contact force is sensed using Hall effect sensors and embedded magnets. The paper presents the design and fabrication of these sensors, the communication protocol and multiplexing scheme used to allow an interactive rate of communication with a high-level controller, and an evaluation of these sensors for actively mapping the shape of the environment and compliance control using gestures and contact with the robot. Characterization of the proximity sensors is presented with considerations of sensitivity to lighting, color, and texture conditions. Also, characterization of the force sensing is presented. The results show that the multi-modal sensory array can enable pre and post-collision active safety measures and can also enable user interaction with the robot. We believe this new technology allows for increased safety for human-robot interaction in confined and semi-structures spaces due to its demonstrated capabilities of detecting impending collision and mapping the environment along the length of the robot. Future miniaturization of the electronics will also allow possible integration in smaller continuum and soft robots.

Toward safe and high-performance human–robot collaboration via implementation of redundancy and understanding the effects of admittance term parameters

Today, demandsin industrial manufacturing mandate humans to work with large-scale industrial robots, and this collaboration may result in dangerous conditions for humans. To deal with this situation, this work proposes a novel approach for redundant large-scale industrial robots. In the proposed approach, an admittance controller is designed to regulate the interaction between the end effector of the robot and the human. Additionally, an obstacle avoidance algorithm is implemented in the null space of the robot to prevent any possible unexpected collision between the human and the links of the robot. After safety performance of this approach is verified via simulations and experimental studies, the effect of the parameters of the admittance controller on the performance of collaboration in terms of both accuracy and total human effort is investigated. This investigation is carried out via 8 experiments by the participation of 10 test subjects in which the effect of different admittance controller parameters such as mass and damper are compared. As a result of this investigation, tuning insights for such parameters are revealed.

User-Interactive Robot Skin with Large-Area Scalability for Safer and Natural Human-Robot Collaboration in Future Telehealthcare

With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.

A Deformable Interface for Human Touch Recognition Using Stretchable Carbon Nanotube Dielectric Elastomer Sensors and Deep Neural Networks

This article presents a machine learning approach to map outputs from an embedded array of sensors distributed throughout a deformable body to continuous and discrete virtual states, and its application to interpret human touch in soft interfaces. We integrate stretchable capacitors into a rubber membrane, and use a passive addressing scheme to probe sensor arrays in real time. To process the signals from this array, we feed capacitor measurements into convolutional neural networks that classify and localize touch events on the interface. We implement this concept with a device called OrbTouch. To modularize the system, we use a supervised learning approach wherein a user defines a set of touch inputs and trains the interface by giving it examples; we demonstrate this by using OrbTouch to play the popular game Tetris. Our regression model localizes touches with mean test error of 0.09 mm, whereas our classifier recognizes five gestures with a mean test error of 1.2%. In a separate demonstration, we show that OrbTouch can discriminate between 10 different users with a mean test error of 2.4%. At test time, we feed the outputs of these models into a debouncing algorithm to provide a nearly error-free experience.

Materials that make robots smart

We posit that embodied artificial intelligence is not only a computational, but also a materials problem. While the importance of material and structural properties in the control loop are well understood, materials can take an active role during control by tight integration of sensors, actuators, computation, and communication. We envision such materials to abstract functionality, therefore making the construction of intelligent robots more straightforward and robust. For example, robots could be made of bones that measure load, muscles that move, skin that provides the robot with information about the kind and location of tactile sensations ranging from pressure to texture and damage, eyes that extract high-level information, and brain material that provides computation in a scalable manner. Such materials will not resemble any existing engineered materials, but rather the heterogeneous components out of which their natural counterparts are made. We describe the state-of-the-art in so-called “robotic materials,” their opportunities for revolutionizing applications ranging from manipulation to autonomous driving by describing two recent robotic materials, a smart skin and a smart tire in more depth, and conclude with open challenges that the robotics community needs to address in collaboration with allies, such as wireless sensor network researchers and polymer scientists.