Programmable Shape-Shifting Soft Robotic Structure Using Liquid Metal Electromagnetic Actuators.

Constant development of soft robots, stretchable electronics, or flexible medical devices forces the research to look for new flexible structures that can change their shapes under external physical stimuli. This study presents a soft robotic structure that can change its shape into different three-dimensional (3D) configurations in response to electric current flown through the embedded liquid-metal conductors enabling electromagnetic actuation. The proposed structure is composed of volumetric pixels (voxels) connected in series where each can be independently controlled by the inputs of electrical current and vacuum pressure. A single voxel is made up of a granular core (GC) with an outer shell made of silicone rubber. The shell has embedded channels filled with liquid metal. The structure changes its shape under the Lorentz force produced by the liquid metal channel under applied electrical current. The GC allows the structure to maintain its shape after deformation even when the current is shut off. This is possible due to the granular jamming effect. In this study, we show the concept, the results of multiphysics simulation, and experimental characterization, including among other techniques, such as 3D digital image correlation or 3D magnetic field scanning, to study the different properties of the structure. We prove that the proposed structure can morph into many different shapes with the amplitude higher than 10 mm, and this process can be both fully reversible and repeatable.

Liquid Crystal Elastomer Based Dexterous Artificial Motor Unit.

Despite the great advancement in designing diverse soft robots, they are not yet as dexterous as animals in many aspects. One challenge is that they still lack the compact design of an artificial motor unit with a great comprehensive performance that can be conveniently fabricated, although many recently developed artificial muscles have shown excellent properties in one or two aspects. Herein, an artificial motor unit is developed based on gold-coated ultrathin liquid crystal elastomer (LCE) film. Subject to a voltage, Joule heating generated by the gold film increases the temperature of the LCE film underneath and causes it to contract. Due to the small thermal inertial and electrically controlling method of the ultrathin LCE structure, its cyclic actuation speed is fast and controllable. It is shown that under electrical stimulation, the actuation strain of the LCE-based motor unit reaches 45%, the strain rate reaches 750%/s, and the output power density is as high as 1360 W kg-1 . It is further demonstrated that the LCE-based motor unit behaves like an actuator, a brake, or a nonlinear spring on demand, analogous to most animal muscles. Finally, as a proof-of-concept, multiple highly dexterous artificial neuromuscular systems are demonstrated using the LCE-based motor unit.

Explainable Deep Learning Model for EMG-Based Finger Angle Estimation Using Attention.

Electromyography (EMG) is one of the most common methods to detect muscle activities and intentions. However, it has been difficult to estimate accurate hand motions represented by the finger joint angles using EMG signals. We propose an encoder-decoder network with an attention mechanism, an explainable deep learning model that estimates 14 finger joint angles from forearm EMG signals. This study demonstrates that the model trained by the single-finger motion data can be generalized to estimate complex motions of random fingers. The color map result of the after-training attention matrix shows that the proposed attention algorithm enables the model to learn the nonlinear relationship between the EMG signals and the finger joint angles, which is explainable. The highly activated entries in the color map of the attention matrix derived from model training are consistent with the experimental observations in which certain EMG sensors are highly activated when a particular finger moves. In summary, this study proposes an explainable deep learning model that estimates finger joint angles based on EMG signals of the forearm using the attention mechanism.

Triboresistive Touch Sensing: Grid-Free Touch-Point Recognition Based on Monolayered Ionic Power Generators.

Recent growing pursuit of skin-mountable devices has been impeded by the complicated structures of most sensing systems, containing electrode grids, stacked multilayers, and even external power sources. Here, a type of touch sensing, termed "triboresistive touch sensing", is introduced for gridless touch recognition based on monolayered ionic power generators. A homogeneous monolayer, i.e., ionic poly(dimethylsiloxane) (PDMS), generates electricity based on the electric field generated by touch. Voltages generated at each corner of the ionic PDMS rely on resistance between touch points and each corner, ensuring recognition of the touch positions without the need for electrode grid layers and external power sources. With notable advantages of high transparency (96.5%), stretchability (539.1%), and resilience (99.0%) of the ionic PDMS, epidermal triboresistive sensing is demonstrated to express touch position and readily play a musical instrument. A gridless system of triboresistive sensing allows rearrangement of the touch sections according to a given situation without any physical modification, and thus easily completes consecutive missions of controlling position, orientation, and gripping functions of a robot.

Smart Skin: Vision-Based Soft Pressure Sensing System for In-Home Hand Rehabilitation.

We introduce a novel in-home hand rehabilitation system for monitoring hand motions and assessing grip forces of stroke patients. The overall system is composed of a sensing device and a computer vision system. The sensing device is a lightweight cylindrical object for easy grip and manipulation, which is covered by a passive sensing layer called "Smart Skin." The Smart Skin is fabricated using soft silicone elastomer, which contains embedded microchannels partially filled with colored fluid. When the Smart Skin is compressed by grip forces, the colored fluid rises and fills in the top surface display area. Then, the computer vision system captures the image of the display area through a red-green-blue camera, detects the length change of the liquid through image processing, and eventually maps the liquid length to the calibrated force for estimating the gripping force. The passive sensing mechanism of the proposed Smart Skin device works in conjunction with a single camera setup, making the system simple and easy to use, while also requiring minimum maintenance effort. Our system, on one hand, aims to support home-based rehabilitation therapy with minimal or no supervision by recording the training process and the force data, which can be automatically conveyed to physical therapists. In contrast, the therapists can also remotely instruct the patients with their training prescriptions through online videos. This study first describes the design, fabrication, and calibration of the Smart Skin, and the algorithm for image processing, and then presents experimental results from the integrated system. The Smart Skin prototype shows a relatively linear relationship between the applied force and the length change of the liquid in the range of 0-35 N. The computer vision system shows the estimation error <4% and a relatively high stability in estimation under different hand motions.

Soft artificial electroreceptors for noncontact spatial perception.

Elasmobranch fishes, such as sharks, skates, and rays, use a network of electroreceptors distributed on their skin to locate adjacent prey. The receptors can detect the electric field generated by the biomechanical activity of the prey. By comparing the intensity of the electric fields sensed by each receptor in the network, the animals can perceive the relative positions of the prey without making physical contact. Inspired by this capacity for prey localization, we developed a soft artificial electroreceptor that can detect the relative positions of nearby objects in a noncontact manner by sensing the electric fields that originate from the objects. By wearing the artificial receptor, one can immediately receive spatial information of a nearby object via auditory signals. The soft artificial electroreceptor is expected to expand the ways we can perceive space by providing a sensory modality that did not evolve naturally in human beings.

Body Caudal Undulation Measured by Soft Sensors and Emulated by Soft Artificial Muscles.

We propose the use of bio-inspired robotics equipped with soft sensor technologies to gain a better understanding of the mechanics and control of animal movement. Soft robotic systems can be used to generate new hypotheses and uncover fundamental principles underlying animal locomotion and sensory capabilities, which could subsequently be validated using living organisms. Physical models increasingly include lateral body movements, notably back and tail bending, which are necessary for horizontal plane undulation in model systems ranging from fish to amphibians and reptiles. We present a comparative study of the use of physical modeling in conjunction with soft robotics and integrated soft and hyperelastic sensors to monitor local pressures, enabling local feedback control, and discuss issues related to understanding the mechanics and control of undulatory locomotion. A parallel approach combining live animal data with biorobotic physical modeling promises to be beneficial for gaining a better understanding of systems in motion.

Review of machine learning methods in soft robotics.

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Soft Miniaturized Actuation and Sensing Units for Dynamic Force Control of Cardiac Ablation Catheters.

Recently, there has been active research in finding robotized solutions for the treatment of atrial fibrillation (AF) by augmenting catheter systems through the integration of force sensors at the tip. However, limited research has been aimed at providing automatic force control by also integrating actuation of the catheter tip, which can significantly enhance safety in such procedures. This article solves the demanding challenge of miniaturizing both actuation and sensing for integration into flexible catheters. Fabrication strategies are presented for a series of novel soft thick-walled cylindrical actuators, with embedded sensing using eutectic gallium-indium. The functional catheter tips have a diameter in the range of 2.6-3.6 mm and can both generate and detect forces in the range of < 0.4 N, with a bandwidth of 1-2 Hz. The deformation modeling of thick-walled cylinders with fiber reinforcement is presented in the article. An experimental setup developed for static and dynamic characterization of these units is presented. The prototyped units were validated with respect to the design specifications. The preliminary force control results indicate that these units can be used in tracking and control of contact force, which has the potential to make AF procedures much safer and more accurate.

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry.

Due to the difficulty of manipulating muscle activation in live, freely swimming fish, a thorough examination of the body kinematics, propulsive performance, and muscle activity patterns in fish during undulatory swimming motion has not been conducted. We propose to use soft robotic model animals as experimental platforms to address biomechanics questions and acquire understanding into subcarangiform fish swimming behavior. We extend previous research on a bio-inspired soft robotic fish equipped with two pneumatic actuators and soft strain sensors to investigate swimming performance in undulation frequencies between 0.3 and 0.7 Hz and flow rates ranging from 0 to 20 c m s in a recirculating flow tank. We demonstrate the potential of eutectic gallium-indium (eGaIn) sensors to measure the lateral deflection of a robotic fish in real time, a controller that is able to keep a constant undulatory amplitude in varying flow conditions, as well as using Particle Image Velocimetry (PIV) to characterizing swimming performance across a range of flow speeds and give a qualitative measurement of thrust force exerted by the physical platform without the need of externally attached force sensors. A detailed wake structure was then analyzed with Dynamic Mode Decomposition (DMD) to highlight different wave modes present in the robot's swimming motion and provide insights into the efficiency of the robotic swimmer. In the future, we anticipate 3D-PIV with DMD serving as a global framework for comparing the performance of diverse bio-inspired swimming robots against a variety of swimming animals.

Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces.

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

Electronic skins and machine learning for intelligent soft robots.

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Ratchet-integrated pneumatic actuator (RIPA): a large-stroke soft linear actuator inspired by sarcomere muscle contraction.

Pneumatic artificial muscles (PAMs) have a wide range of robotics applications, especially in soft robots, for their ability to generate linear force and displacement with the soft, lightweight, compact, and safe characteristics as well as high power densities. However, the compressibility of the air causes a spring-like behavior of PAMs, resulting in several common issues of limited stroke, load-dependent stroke lengths, difficulty in maintaining their length against disturbance, and necessity of accurate pressure control system. To address these issues, this study borrows inspiration from a biological soft linear actuator, a muscle, and proposes a ratchet-integrated pneumatic actuator (RIPA). Utilizing two pawls integrated at both ends of a McKibben muscle and a flexible rack inserted in the middle of the muscle, the RIPA achieves a large stroke length by accumulating displacements from multiple small strokes of the McKibben muscle by repeating the cycle of pressurization and depressurization. This cycle mimics the cross-bridge model of a sarcomere, a basic unit of a skeletal muscle, in which a muscle accumulates nanoscale strokes of myosin head motors to generate large strokes. The synergy between a PAM and the inspiration from a sarcomere enabled a large-stroke soft linear actuator that can generate independent strokes from loads. The proposed actuator is not only capable of maintaining its length against unexpected mechanical disturbances but also controllable with a relatively simple system. In this paper, we describe the design of the RIPA and provide analytical models to predict the stroke length and the period per cycle for actuation. We also present experimental results for characterization and comparison with model predictions.

Soft Wearable Skin-Stretch Device for Haptic Feedback Using Twisted and Coiled Polymer Actuators.

Soft and integrated design can enable wearable haptic devices to augment natural human taction. This paper proposes a novel, soft, haptic finger-worn wearable device based on compliant and adhesive silicone skin and lightweight twisted and coiled polymer (TCP) actuators using ultra high molecular weight polyethylene (UHMWPE) fibers to provide lateral skin stretch sensations. Recently, silicone elastomers have been used in wearable sensors and in haptic applications for their high compliance or adhesion. TCP actuators have also demonstrated high power to weight ratios, large stroke length, simple mechanism, and inherent softness. Lateral skin stretch is sensitive to small motions and has been used for intuitive proprioceptive feedback applications. We combined these characteristics to design and manufacture a wearable, functional haptic prototype. Prototype performance was evaluated using an optical tracking system, a force gauge test bench, and compared to vibrotactile haptic feedback in a experiment with 14 healthy participants. Results showed that participant mean reaction times were comparable to those of a vibrotactile feedback system, though task completion times were longer. This paper is the first to employ TCP actuators for haptic stimulation and could serve as a foundation for future applications involving soft wearable haptics in gaming, health, and virtual reality.

Sensorized, Flat, Pneumatic Artificial Muscle Embedded with Biomimetic Microfluidic Sensors for Proprioceptive Feedback.

In recent years, soft components, such as pneumatic artificial muscles (PAMs), have been increasingly employed to design safer wearable devices. Despite the inherent compliance of the materials used to fabricate PAMs, the actuators are able to produce relatively large forces and work when compared to their weight. However, effective operation of these systems has traditionally required bulky external force and position sensors, which limit the maneuverability of users. To overcome these issues, inspiration was taken from organic muscles, which incorporate embedded sensors, such as Golgi tendon organs and muscle spindles, to provide real-time position and force feedback for muscles. As such, a sensorized, flat, pneumatic artificial muscle (sFPAM) with embedded force and position sensors was designed and fabricated. In addition, a hyperelastic model was developed and verified through comparison with the experimentally characterized mechanical and electrical performance of the sFPAM. Furthermore, a sliding mode controller was implemented to demonstrate the feasibility of embedded sensors to provide feedback during operation. Ultimately, a lightweight, compact actuation system was designed with the ability to be seamlessly incorporated into future wearable devices.

Miniaturized Robotic End-Effector with Piezoelectric Actuation and Fiber Optic Sensing for Minimally Invasive Cardiac Procedures.

Each year 35,000 cardiac ablation procedures are performed to treat atrial fibrillation through the use of catheter systems. The success rate of this treatment is highly dependent on the force which the catheter applies on the heart wall. If the magnitude of the applied force is much higher than a certain threshold the tissue perforates, whereas if the force is lower than this threshold the lesion size may be too large and is inconsistent. Furthermore, studies have shown large variability in the applied force from trained physicians during treatment, suggesting that physicians are unable to manually regulate the levels of the force at the site of treatment. Current catheter systems do not provide the physicians with active means for contact force control and are only at most aided by visual feedback of the forces measured in situ. This paper discusses a novel design of a robotic end-effector that integrates mechanisms of sensing and actively controlling of the applied forces into a miniaturized compact form. The required specifications for design and integration were derived from the current application under investigation. An off-the-shelf miniature piezoelectric motor was chosen for actuation, and a force sensing solution was developed to meet the specifications. Experimental characterization of the actuator and the force sensor within the integrated setup show compliance with the specifications and pave the way for future experimentation where closed-loop control of the system can be implemented according to the contact force control strategies for the application.

Accelerated Curing and Enhanced Material Properties of Conductive Polymer Nanocomposites by Joule Heating.

Joule heating is useful for fast and reliable manufacturing of conductive composite materials. In this study, we investigated the influence of Joule heating on curing conditions and material properties of polymer-based conductive composite materials consisting of carbon nanotubes (CNTs) and polydimethylsiloxane (PDMS). We applied different voltages to the CNT nanocomposites to investigate their electrical stabilization, curing temperature, and curing time. The result showed that highly conductive CNT/PDMS composites were successfully cured by Joule heating with uniform and fast heat distribution. For a 7.0 wt % CNT/PDMS composite, a high curing temperature of around 100 °C was achieved at 20 V with rapid temperature increase. The conductive nanocomposite cured by Joule heating also revealed an enhancement in mechanical properties without changing the electrical conductivities. Therefore, CNT/PDMS composites cured by Joule heating are useful for expediting the manufacturing process for particulate conductive composites in the field of flexible and large-area sensors and electronics, where fast and uniform curing is critical to their performance.

Assessment of Quality of Life and Safety in Postmenopausal Breast Cancer Patients Receiving Letrozole as an Early Adjuvant Treatment.

PURPOSE: There are few reports from Asian countries about the long-term results of aromatase inhibitor adjuvant treatment for breast cancer. This observational study aimed to evaluate the long-term effects of letrozole in postmenopausal Korean women with operable breast cancer. METHODS: Self-reported quality of life (QoL) scores were serially assessed for 3 years during adjuvant letrozole treatment using the Korean version of the Functional Assessment of Cancer Therapy-Breast questionnaires (version 3). Changes in bone mineral density (BMD) and serum cholesterol levels were also examined. RESULTS: All 897 patients received the documented informed consent form and completed a baseline questionnaire before treatment. Adjuvant chemotherapy was administered to 684 (76.3%) subjects, and 410 (45.7%) and 396 (44.1%) patients had stage I and II breast cancer, respectively. Each patient completed questionnaires at 3, 6, 12, 18, 24, 30, and 36 months after enrollment. Of 897 patients, 749 (83.5%) completed the study. The dropout rate was 16.5%. The serial trial outcome index, the sum of the physical and functional well-being subscales, increased gradually and significantly from baseline during letrozole treatment (p<0.001). The mean serum cholesterol level increased significantly from 199 to 205 after 36 months (p=0.042). The mean BMD significantly decreased from -0.39 at baseline to -0.87 after 36 months (p<0.001). CONCLUSION: QoL gradually improved during letrozole treatment. BMD and serum cholesterol level changes were similar to those in Western countries, indicating that adjuvant letrozole treatment is well tolerated in Korean women, with minimal ethnic variation.

Design of a Lightweight Soft Robotic Arm Using Pneumatic Artificial Muscles and Inflatable Sleeves.

As robots begin to interact with humans and operate in human environments, safety becomes a major concern. Conventional robots, although reliable and consistent, can cause injury to anyone within its range of motion. Soft robotics, wherein systems are made to be soft and mechanically compliant, are thus a promising alternative due to their lightweight nature and ability to cushion impacts, but current designs often sacrifice accuracy and usefulness for safety. We, therefore, have developed a bioinspired robotic arm combining elements of rigid and soft robotics such that it exhibits the positive qualities of both, namely compliance and accuracy, while maintaining a low weight. This article describes the design of a robotic arm-wrist-hand system with seven degrees of freedom (DOFs). The shoulder and elbow each has two DOFs for two perpendicular rotational motions on each joint, and the hand has two DOFs for wrist rotations and one DOF for a grasp motion. The arm is pneumatically powered using custom-built McKibben type pneumatic artificial muscles, which are inflated and deflated using binary and proportional valves. The wrist and hand motions are actuated through servomotors. In addition to the actuators, the arm is equipped with a potentiometer in each joint for detecting joint angle changes. Simulation and experimental results for closed-loop position control are also presented in the article.

Magnetically Assisted Bilayer Composites for Soft Bending Actuators.

This article presents a soft pneumatic bending actuator using a magnetically assisted bilayer composite composed of silicone polymer and ferromagnetic particles. Bilayer composites were fabricated by mixing ferromagnetic particles to a prepolymer state of silicone in a mold and asymmetrically distributed them by applying a strong non-uniform magnetic field to one side of the mold during the curing process. The biased magnetic field induces sedimentation of the ferromagnetic particles toward one side of the structure. The nonhomogeneous distribution of the particles induces bending of the structure when inflated, as a result of asymmetric stiffness of the composite. The bilayer composites were then characterized with a scanning electron microscopy and thermogravimetric analysis. The bending performance and the axial expansion of the actuator were discussed for manipulation applications in soft robotics and bioengineering. The magnetically assisted manufacturing process for the soft bending actuator is a promising technique for various applications in soft robotics.