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.

Pioneering healthcare with soft robotic devices: A review.

Recent advancements in soft robotics have been emerging as an exciting paradigm in engineering due to their inherent compliance, safe human interaction, and ease of adaptation with wearable electronics. Soft robotic devices have the potential to provide innovative solutions and expand the horizons of possibilities for biomedical applications by bringing robots closer to natural creatures. In this review, we survey several promising soft robot technologies, including flexible fluidic actuators, shape memory alloys, cable-driven mechanisms, magnetically driven mechanisms, and soft sensors. Selected applications of soft robotic devices as medical devices are discussed, such as surgical intervention, soft implants, rehabilitation and assistive devices, soft robotic exosuits, and prosthetics. We focus on how soft robotics can improve the effectiveness, safety and patient experience for each use case, and highlight current research and clinical challenges, such as biocompatibility, long-term stability, and durability. Finally, we discuss potential directions and approaches to address these challenges for soft robotic devices to move toward real clinical translations in the future.

Hydrogels in Soft Robotics: Past, Present, and Future.

The rise of soft robotics in recent years has motivated significant developments in smart materials (and vice versa), as these materials allow for more compact robotic designs thanks to the embodied intelligence that they provide. Hydrogels have long been postulated as one of the potential candidates to be used in soft robotics due to their softness, elasticity, and smart properties that can be tuned with nanomaterials. However, nowadays they represent only a small percentage of the materials used in the field. In this perspective, the drawbacks that have hindered their utilization so far are analyzed as well as the current state of hydrogel-based soft actuators, sensors, and manufacturing possibilities. The future improvements that need to be made to achieve a real application of hydrogels in soft robotics are also discussed.

Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation.

Despite their high prediction accuracy, deep learning-based soft sensor (DLSS) models face challenges related to adversarial robustness against malicious adversarial attacks, which hinder their widespread deployment and safe application. Although adversarial training is the primary method for enhancing adversarial robustness, existing adversarial-training-based defense methods often struggle with accurately estimating transfer gradients and avoiding adversarial robust overfitting. To address these issues, we propose a novel adversarial training approach, namely domain-adaptive adversarial training (DAAT). DAAT comprises two stages: historical gradient-based adversarial attack (HGAA) and domain-adaptive training. In the first stage, HGAA incorporates historical gradient information into the iterative process of generating adversarial samples. It considers gradient similarity between iterative steps to stabilize the updating direction, resulting in improved transfer gradient estimation and stronger adversarial samples. In the second stage, a soft sensor domain-adaptive training model is developed to learn common features from adversarial and original samples through domain-adaptive training, thereby avoiding excessive leaning toward either side and enhancing the adversarial robustness of DLSS without robust overfitting. To demonstrate the effectiveness of DAAT, a DLSS model for crystal quality variables in silicon single-crystal growth manufacturing processes is used as a case study. Through DAAT, the DLSS achieves a balance between defense against adversarial samples and prediction accuracy on normal samples to some extent, offering an effective approach for enhancing the adversarial robustness of DLSS.

Soft bioreactor systems: a necessary step toward engineered MSK soft tissue?

A key objective of tissue engineering (TE) is to produce in vitro funcional grafts that can replace damaged tissues or organs in patients. TE uses bioreactors, which are controlled environments, allowing the application of physical and biochemical cues to relevant cells growing in biomaterials. For soft musculoskeletal (MSK) tissues such as tendons, ligaments and cartilage, it is now well established that applied mechanical stresses can be incorporated into those bioreactor systems to support tissue growth and maturation via activation of mechanotransduction pathways. However, mechanical stresses applied in the laboratory are often oversimplified compared to those found physiologically and may be a factor in the slow progression of engineered MSK grafts towards the clinic. In recent years, an increasing number of studies have focused on the application of complex loading conditions, applying stresses of different types and direction on tissue constructs, in order to better mimic the cellular environment experienced in vivo. Such studies have highlighted the need to improve upon traditional rigid bioreactors, which are often limited to uniaxial loading, to apply physiologically relevant multiaxial stresses and elucidate their influence on tissue maturation. To address this need, soft bioreactors have emerged. They employ one or more soft components, such as flexible soft chambers that can twist and bend with actuation, soft compliant actuators that can bend with the construct, and soft sensors which record measurements in situ. This review examines types of traditional rigid bioreactors and their shortcomings, and highlights recent advances of soft bioreactors in MSK TE. Challenges and future applications of such systems are discussed, drawing attention to the exciting prospect of these platforms and their ability to aid development of functional soft tissue engineered grafts.

Soft Sensory-Motor System Based on Ionic Solution for Robotic Applications.

Soft robots claim the architecture of actuators, sensors, and computation demands with their soft bodies by obtaining fast responses and adapting to the environment. Sensory-motor coordination is one of the main design principles utilized for soft robots because it allows the capability to sense and actuate mutually in the environment, thereby achieving rapid response performance. This work intends to study the response for a system that presents coupled actuation and sensing functions simultaneously and is integrated in an arbitrary elastic structure with ionic conduction elements, called as soft sensory-motor system based on ionic solution (SSMS-IS). This study provides a comparative analysis of the performance of SSMS-IS prototypes with three diverse designs: toroidal, semi-toroidal, and rectangular geometries, based on a series of performance experiments, such as sensitivity, drift, and durability. The design with the best performance was the rectangular SSMS-IS using silicon rubber RPRO20 for both internal and external pressures applied in the system. Moreover, this work explores the performance of a bioinspired soft robot using rectangular SSMS-IS elements integrated in its body. Further, it investigated the feasibility of the robot to adapt its morphology online for environment variability, responding to external stimuli from the environment with different levels of stiffness and damping.

Probing with Each Step: How a Walking Crab-like Robot Classifies Buried Cylinders in Sand with Hall-Effect Sensors.

Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in load bearing compliant segments. The magnetometers are sensitive to magnetic objects in close proximity to the legs and their movement relative to embedded magnets, allowing for both proximity and force-related feedback in dynamically obtained measurements. A dataset of three-axis measurements is collected as the robot steps near and over different UXOs and UXO-like objects, and a convolutional neural network is trained on time domain inputs and evaluated by 5-fold cross validation. Additionally, we propose a novel method for interpreting the importance of measurements in the time domain for the trained classifier. The results demonstrate the potential for accurate and efficient UXO and non-UXO discrimination in the field.

Dual-network carboxymethyl chitosan conductive hydrogels for multifunctional sensors and high-performance triboelectric nanogenerators.

With the development of technology, there is a growing demand for wearable electronics that can fulfill different application scenarios. Hydrogel-based sensors are considered ideal candidates for realizing multifunctional wearable flexible devices. However, there are great challenges in preparing hydrogel-based sensors with both superior mechanical and electrical properties. Herein, we report a composite conductive hydrogel prepared by using a dynamically crosslinked carboxymethyl chitosan network and a covalently crosslinked polymer network, and carboxylated carbon nanotubes as conductive filler. The carboxymethyl chitosan-based hydrogels had excellent mechanical properties and strength (tensile strength of 475.4 kPa, and compressive strength of 1.9 MPa) and ultra-high conductivity (0.19 S·cm-1). Based on the above characteristics, the hydrogel could accurately identify the movement signals of the human body and different writing signals, and achieve encrypted transmission of signals, broadening the application scenarios. In addition, a triboelectric nanogenerator (TENG) was fabricated based on the hydrogel, which had an outstanding output performance with open-circuit voltage of 336 V, short-circuit current of 18 µA, transferred charge of 52 nC and maximum power density of 340 mW·m-2, and could power small devices. This work is expected to provide new ideas for the development of self-powered, multi-functional wearable, and flexible polysaccharide-based devices.

A Sensorized Soft Robotic Hand with Adhesive Fingertips for Multimode Grasping and Manipulation.

Soft robotic grippers excel at achieving conformal and reliable contact with objects without the need for complex control algorithms. However, they still lack in grasp and manipulation abilities compared with human hands. In this study, we present a sensorized multi-fingered soft gripper with bioinspired adhesive fingertips that can provide both fingertip-based adhesion grasping and finger-based form closure grasping modes. The gripper incorporates mushroom-like microstructures on its adhesive fingertips, enabling robust adhesion through uniform load shearing. A single fingertip exhibits a maximum load capacity of 4.18 N against a flat substrate. The soft fingers have multiple joints, and each joint can be independently actuated through pneumatic control. This enables diverse bending motions and stable grasping of various objects, with a maximum load capacity of 28.29 N for three fingers. In addition, the soft gripper is equipped with a kirigami-patterned stretchable sensor for motion monitoring and control. We demonstrate the effectiveness of our design by successfully grasping and manipulating a diverse range of objects with varying shapes, sizes, and curvatures. Moreover, we present the practical application of our sensorized soft gripper for remotely controlled cooking.

Soft touchless sensors and touchless sensing for soft robots.

Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity.

Integrated Sensors for Soft Medical Robotics.

Minimally invasive procedures assisted by soft robots for surgery, diagnostics, and drug delivery have unprecedented benefits over traditional solutions from both patient and surgeon perspectives. However, the translation of such technology into commercialization remains challenging. The lack of perception abilities is one of the obstructive factors paramount for a safe, accurate and efficient robot-assisted intervention. Integrating different types of miniature sensors onto robotic end-effectors is a promising trend to compensate for the perceptual deficiencies in soft robots. For example, haptic feedback with force sensors helps surgeons to control the interaction force at the tool-tissue interface, impedance sensing of tissue electrical properties can be used for tumor detection. The last decade has witnessed significant progress in the development of multimodal sensors built on the advancement in engineering, material science and scalable micromachining technologies. This review article provides a snapshot on common types of integrated sensors for soft medical robots. It covers various sensing mechanisms, examples for practical and clinical applications, standard manufacturing processes, as well as insights on emerging engineering routes for the fabrication of novel and high-performing sensing devices.

Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants.

Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems.

Machine learning for modeling N2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability.

Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating N2O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N2O emissions hold substantial potential. However, so far, a comprehensive approach is still overlooked, impeding the advancement of full-scale application. Therefore, this study develops a comprehensive approach for using machine learning to perform online process modeling of N2O emissions. The approach is tested on a long-term N2O emission dataset from a full-scale WWTP. Uniquely, the proposed approach emphasizes not just model accuracy, but it also considers model complexity, computational speed, and interpretability, equipping operators with the insights needed for informed corrective actions. Algorithms with varying levels of complexity and interpretability including k-Nearest Neighbors (kNN), decision trees, ensemble learning models, and deep neural networks (DNN) were considered. Furthermore, a parametric multivariate outlier removal method was adjusted to account for data statistical distributions, significantly reducing data loss. By employing an effective feature selection methodology, a trade-off between data acquisition, model performance, and complexity was found, reducing the number of features by 40 % and decreasing data collection cost, model complexity and computational burden without significant effect on modeling accuracy. The best performing models are kNN (R2 = 0.88), AdaBoost (R2 = 0.94), and DNN (R2 = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N2O mitigation decisions.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8 mm, depth errors of 0.22 mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Soft Sensing of LPG Processes Using Deep Learning.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery's LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

Soft implantable printed bioelectronic system for wireless continuous monitoring of restenosis.

Atherosclerosis is a prominent cause of coronary artery disease and broader cardiovascular diseases, the leading cause of death worldwide. Angioplasty and stenting is a common treatment, but in-stent restenosis, where the artery re-narrows, is a frequent complication. Restenosis is detected through invasive procedures and is not currently monitored frequently for patients. Here, we report an implantable vascular bioelectronic device using a newly developed miniaturized strain sensor via microneedle printing methods. A capillary-based printing system achieves high-resolution patterning of a soft, capacitive strain sensor. Ink and printing parameters are evaluated to create a fully printed sensor, while sensor design and sensing mechanism are studied to enhance sensitivity and minimize sensor size. The sensor is integrated with a wireless vascular stent, offering a biocompatible, battery-free, wireless monitoring system compatible with conventional catheterization procedures. The vascular sensing system is demonstrated in an artery model for monitoring restenosis progression. Collectively, the artery implantable bioelectronic system shows the potential for wireless, real-time monitoring of various cardiovascular diseases and stent-integrated sensing/treatments.

Recent Advances in Sensor-Actuator Hybrid Soft Systems: Core Advantages, Intelligent Applications, and Future Perspectives.

The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft transducers. Among soft systems, sensor-actuator hybrid systems are considered the most promising due to their effective and efficient performance, resulting from the synergistic and complementary interaction between their sensor and actuator components. Recent research on integrated sensor and actuator systems has resulted in a range of conceptual and practical soft systems. This review article provides a comprehensive analysis of recent advances in sensor and actuator integrated systems, which are grouped into three categories based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for a hybrid of intelligent detection and movement. In addition, several bottlenecks in current studies are discussed, and prospective outlooks, including potential applications, are presented. This categorization and analysis will pave the way for the advancement and commercialization of sensor and actuator-integrated systems.

An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors.

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator's outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.

Heterogeneous Multi-Material Flexible Piezoresistive Sensor with High Sensitivity and Wide Measurement Range.

Flexible piezoresistive sensors (FPSs) have the advantages of compact structure, convenient signal acquisition and fast dynamic response; they are widely used in motion detection, wearable electronic devices and electronic skins. FPSs accomplish the measurement of stresses through piezoresistive material (PM). However, FPSs based on a single PM cannot achieve high sensitivity and wide measurement range simultaneously. To solve this problem, a heterogeneous multi-material flexible piezoresistive sensor (HMFPS) with high sensitivity and a wide measurement range is proposed. The HMFPS consists of a graphene foam (GF), a PDMS layer and an interdigital electrode. Among them, the GF serves as a sensing layer, providing high sensitivity, and the PDMS serves as a supporting layer, providing a large measurement range. The influence and principle of the heterogeneous multi-material (HM) on the piezoresistivity were investigated by comparing the three HMFPS with different sizes. The HM proved to be an effective way to produce flexible sensors with high sensitivity and a wide measurement range. The HMFPS-10 has a sensitivity of 0.695 kPa-1, a measurement range of 0-14,122 kPa, fast response/recovery (83 ms and 166 ms) and excellent stability (2000 cycles). In addition, the potential application of the HMFPS-10 in human motion monitoring was demonstrated.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.