Bending Sensor Research Articles

Bending response performance in nitrogen-doped reduced graphene oxide-PEDOT:PSS: The impact of nitrogen flow rate on the nitrogen doping configurations

Mechanical bending sensors play a pivotal role in various applications, from wearable devices to healthcare monitoring systems. The integration of nitrogen-doped reduced graphene oxide (NrGO) materials has emerged as a versatile approach, offering a promising avenue for enhancing the performances of these sensors. Various nitrogen doping configurations within the materials matrix have been found to profoundly influence the sensor's bending response. In this work, we identified that pyrrolic-N doping configurations were more dominant at lower nitrogen flow rates of 10 and 20 sccm, with the percentages of 52.9 and 48.7 %, respectively. These configurations exhibited a stable pattern of resistance changes in response to bending, particularly when subjected to bending angles of 55° and 65°. Despite some inconsistencies in bending response at lower bending angles, the sensors demonstrated heightened sensitivity, registering at 0.34 kPa. In contrast, sensors predominantly characterized by pyridinic-N configurations at 40 sccm maintained a consistent level of sensitivity across different bending angles, demonstrating remarkable stability and structural robustness, even after enduring 10,000 cycles. These findings indicated that pyridinic-N configurations play a critical role in enhancing sensor performance, ensuring reliable measurements across various mechanical deformations.

Optimizing actual PID control for walking quadruped soft robots using genetic algorithms

The construction of soft robots’s models and controllers remains a significant challenge. In this paper, we propose a new walking control method for the quadruped soft robot named genetic algorithm-optimized PID. First, we construct the control model correlating valve voltage with leg bending based on the geometrical analysis. This modeling approach leverages the characteristics of novel leg structure and bend sensor, thereby streamlining the control model for locomotion of quadruped soft robotic. Moreover, We apply the genetic algorithm to automatically tune parameters and optimize PID controllers, aiming to enhance control performance. The application of the proposed method to the walking control has been uniquely demonstrated on a real 3D-printed quadruped soft robot. Experimental results indicate that the genetic algorithm-optimized PID controller significantly improves trajectory tracking compared to the Ziegler-Nichols tuning method. This optimization increases the robot’s walking speed from 5 mm/s to 8 mm/s, reduces the error rate by 2.4064%, decreases overshoot by 12.55%, and shortens response time by 0.5 s, substantially enhancing the controller’s overall performance. Additionally, compared to particle swarm optimization, the proposed method further improves performance by reducing the error rate by 0.4079%, overshoot by 8.4%, and response time by 1.0 s.

Manufacturing strategies for highly sensitive and self-powered piezoelectric and triboelectric tactile sensors

Abstract Piezoelectric and triboelectric effects are of growing interest for facilitating high-sensitivity and self-powered tactile sensor applications. The working principles of piezoelectric and triboelectric nanogenerators provide strategies for enhancing output voltage signals to achieve high sensitivity. Increasing the piezoelectric constant and surface triboelectric charge density are key factors in this enhancement. Methods such as annealing processes, doping techniques, grain orientation controls, crystallinity controls, and composite structures can effectively enhance the piezoelectric constant. For increasing triboelectric output, surface plasma treatment, charge injection, microstructuring, control of dielectric constant, and structural modification are effective methods. The fabrication methods present significant opportunities in tactile sensor applications. This review article summarizes the overall piezoelectric and triboelectric fabrication processes from materials to device aspects. It highlights applications in pressure, touch, bending, texture, distance, and material recognition sensors. The conclusion section addresses challenges and research opportunities, such as limited flexibility, stretchability, decoupling from multi-stimuli, multifunctional sensors, and data processing.

Two-dimensional vector bending sensor based on single excessively tilted fiber grating

Two-dimensional vector bending sensor based on single excessively tilted fiber grating

Research on vector bending sensors based on taper-drawn seven-core fiber Bragg grating

We present a novel vector bending sensor that enables simultaneous measurement of the bending response in six outer cores of a seven-core fiber (SCF) without the need for fan-in/fan-out configurations. Utilizing femtosecond laser technology, we inscribe seven Fiber Bragg Gratings (FBGs) with distinct wavelengths across the SCF cores. The sensor’s tapering process, tailored for the bending sensing scenario, effectively combines the reflection peaks of the FBGs into one detection channel, thereby substantially improving the measurement efficiency. Our experimental results demonstrate a strong directional dependence of the bending response, with a maximum sensitivity of 127 pm/m−1. The bending angle and curvature magnitude are reconstructed by analyzing the wavelength shifts of any two non-diagonal outer cores, offering a versatile solution for real-time monitoring applications. This sensor design, by eliminating the need for additional fan-in/fan-out devices, simplifies the system architecture and reduces both measurement time and sensor size, making it highly suitable for applications in precision manufacturing, environmental monitoring, and robotics.

High-Performance Sensing Platform Based on Morphology/Lattice Collaborative Control of Femtosecond-Laser-Induced MXene-Composited Graphene.

Flexible sensors based on laser-induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond-laser-induced MXene-composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump-probe detection, laser-induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 Sm-1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48µM. Additionally, LIMG-based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors.

Fabrication of rare earth-doped ZnO-PVDF flexible nanocomposite films for ferroelectric response and their application in piezo-responsive bending sensors.

The present study covers the fabrication of flexible piezoelectric nanogenerators and their application towards sustainable power generation. The rod-like structure of erbium-doped ZnO (EZ) nanoparticles prepared by the hydrothermal synthesis route was successfully incorporated inside the polyvinylidene fluoride (PVDF) matrix using the solution casting method. Solution casting is an easy and cost-effective method for fabricating laminated, thin, flexible and lightweight EZ-PVDF nanocomposite films. The formation of the desired crystallographic phase of EZ-PVDF nanocomposite films and the presence of rod-like EZ nanoparticles inside the PVDF matrix were confirmed using X-ray diffraction and FESEM. The enhancement of the β-phase fraction of the EZ-PVDF nanocomposite films as compared to bare PVDF was estimated using FTIR spectroscopy. The presence of a ferroelectric phase in the EZ-PVDF nanocomposite films was found due to the formation of a large area of interfaces between the EZ nanoparticles and the PVDF matrix. The maximum polarizations of 0.00696 μC cm-2 and 0.00683 μC cm-2 for two samples (EZP1 and EZP2, respectively) were observed at an electric field of 1.25 kV cm-1. The piezoelectric voltages were observed at relatively low frequencies for both nanocomposite films. The maximum piezoelectric voltages of 18.9 V and 15.5 V were observed at a 1 Hz frequency for EZP1 and EZP2, respectively. The output piezoelectric current of 16.88 mA and the maximum power density of 7773.68 W m-3 for EZP1 ensure its potential as an efficient piezoelectric nanogenerator with greater efficiency than those reported previously in published articles. The change in the piezoelectric voltage response of the nanocomposite films as a function of mechanical movement of human external body parts renders them the most suitable candidate for human-machine interfacing (HMI) applications, such as bending sensors and human motion sensors.

Wearable Surface Deformation Myography (sDMG) System for Recognition of Locomotion Modes.

This study designs a wearable sensing system for locomotion mode recognition using lower-limb skin surface curvature deformation caused by the morphological changes of musculotendinous complexes and soft tissues. Flexible bending sensors are embedded into stretch pants, enabling curvature deformations of specific skin segments above lower-limb muscle groups to be captured in a noncontact manner. To evaluate the performance of this system, we conducted experiments on eight able-bodied subjects completing seven common locomotive activities, including walking, running, ramp ascending/descending, stair ascending/descending, and standing. The system measured seven channels of deformation signals from two cross-sections on the shank and the thigh. The collected signals were distinguishable across different locomotion modes and exhibited consistency when monitoring steps. Using selected time-domain features and a linear discriminant analysis (LDA) classifier enabled the proposed system to continuously recognize locomotion modes with an average accuracy of 96.5%. Furthermore, the system maintains recognition performance with 95.7% accuracy even after removing and reapplying the sensors. Finally, we conducted comparison experiments to analyze how window length, feature selection, and the number of channels affect recognition performance, providing insights for optimization. We believe that this novel signal platform holds great potential as a valuable supplementary tool in wearable human motion detection, enriching the information diversity for motion analysis, and enabling new possibilities for further advancements and applications in fields including biomedical engineering, textiles, and computer graphics.

A Planar Coil-Based Inductive Bend Sensor for Soft Robotic Applications

A Planar Coil-Based Inductive Bend Sensor for Soft Robotic Applications

Extremely Stable, Multidirectional, All‐in‐One Piezoelectric Bending Sensor with Cycle up to Million Level

AbstractFlexible bending sensors are crucial components of flexible/wearable electronics. However, their broad application is restricted by their difficulty in sensing different bending directions and, more importantly, their significant output attenuation after long cycles. Herein, a new design of an All‐in‐One piezoelectric bending sensor is proposed, in which double‐deck, cross‐over 3D interdigital microelectrodes (silver nanowires (Ag NWs)) are embedded in a piezoelectric polymer film (poly(vinylidene fluoride‐trifluoroethylene (P(VDF‐TrFE))). The piezoelectric film with the embedded 3D microelectrodes exhibits a high anisotropy coefficient ( > 1) based on dual piezoelectric modes (d31 and d33), which renders it more sensitive to different bending directions. More importantly, the homogeneous interconnected interface and heterogeneous microinterlocked interface formed inside the sensor significantly enhance its interface mechanical properties, with at least a tensile strength of 51 MPa, a shear strength of 28 MPa, and an interfacial toughness of 300 J m−2, which are nearly two orders of magnitude greater than those of conventional sandwich architectures. The prepared All‐in‐One sensor shows an extremely stable piezoelectric output over as many as 16 00 000 bending cycles, which represents a remarkable breakthrough. Furthermore, a single sensor can be used to remotely control the multidirectional motion of a smart car, demonstrating enormous potential in practical applications.

Finite element analysis based verification of shape estimation performance according to displacement conditions and module types of FBG bending sensor

This study proposes a novel, versatile sensor application solution suitable for various environments by verifying the shape estimation performance of three different module types of an FBG bending sensor based on finite element analysis. FBG bending sensors, including the individual attachment type, triangular stacking type, and band type, were attached to the structure. Subsequently, two simple plane displacement and one complex 3D displacement were applied to the structure to derive the strain for nine cases through finite element analysis. The shape estimation performance was then verified through a simulation based on the previously derived strain. As a result of verifying the shape estimation performance, it was confirmed that all three module types had high performance under simple plane displacement condition, whereas individual attachment types had better performance under complex 3D displacement condition. However, it’s important to note that the triangular stacking type and band type, with their small spacing between FBGs, have been developed to overcome the limited attachment conditions of the individual attachment type and to streamline the manufacturing process of the FBG bending sensors. Therefore, it is possible to select the FBG bending sensor according to the installation conditions of the structure to be applied and the required sensor performance by comprehensively considering the performance verification results of this study and the characteristics of the module types.

Gesture-Controlled Robotic Arm for Agricultural Harvesting Using a Data Glove with Bending Sensor and OptiTrack Systems.

This paper presents a gesture-controlled robotic arm system designed for agricultural harvesting, utilizing a data glove equipped with bending sensors and OptiTrack systems. The system aims to address the challenges of labor-intensive fruit harvesting by providing a user-friendly and efficient solution. The data glove captures hand gestures and movements using bending sensors and reflective markers, while the OptiTrack system ensures high-precision spatial tracking. Machine learning algorithms, specifically a CNN+BiLSTM model, are employed to accurately recognize hand gestures and control the robotic arm. Experimental results demonstrate the system's high precision in replicating hand movements, with a Euclidean Distance of 0.0131 m and a Root Mean Square Error (RMSE) of 0.0095 m, in addition to robust gesture recognition accuracy, with an overall accuracy of 96.43%. This hybrid approach combines the adaptability and speed of semi-automated systems with the precision and usability of fully automated systems, offering a promising solution for sustainable and labor-efficient agricultural practices.

Mechanoreception of pneumatic soft robotic finger without tactile sensor based on dual-position feature

Purpose Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors. Design/methodology/approach This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness. Findings Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers. Originality/value This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

Refractive insensitive directional bend sensor based on specialty microstructure optical fiber with dumbbell shape core

With the development of information society, more and more special occasions are calling for a wide range of sensors with special properties. In this work, a refractive index insensitive directional bend sensor is proposed based on a specialty microstructure optical fiber with dumbbell shape core, which works as a dual core fiber (DCF). Firstly, this specialty microstructure optical fiber with dumbbell shape core has been fabricated with self-pressurised drawing technique. Then, it has been constructed as a Mach-Zehnder interferometer (MZI) sensor. Consequently, its bend sensing characteristics has been investigated from both theory and experiment. The response of the proposed sensor to the bending evidently displays the directional dependence. In the bending along x direction, the interference peak wavelength is almost insensitive to the curvature. But the transmission is sensitive, which almost obeys the exponential function. Correspondingly, in the bending along y direction the wavelength almost linearly varies with curvature, giving out an averaged sensitivity of 2.44 nm/m−1 in the curvature range of 0–3.79 m−1. But the peak transmission fluctuates without typical principle. In addition, the response of the proposed sensor to the refractive index variation has been checked. It is found that the peak wavelength is almost insensitive to the change of refractive index from 1.33 to 1.45, and the peak transmission fluctuates without clear principle. All these results demonstrate that the proposed sensor has great potential for future internet of things (IoT) applications.

Omnidirectional and Size-Adaptive Soft Bending Sensor for Accurate Human Joint Motion Monitoring

Omnidirectional and Size-Adaptive Soft Bending Sensor for Accurate Human Joint Motion Monitoring

Directional bending sensor based on Mach-Zehnder interferometer formed by side-polished multimode fiber

This work proposes a fiber bending sensor based on the Mach-Zehnder interferometer (MZI) constructed using a side-polished multimode fiber (MMF) with bending direction recognition. The sensor is fabricated by directly fusing a section of side-polished MMF sandwiched between two no-core fibers (NCFs) to an input and output single-mode fibers (SMFs). As the applied curvature varies from 0 m−1 to 1.64 m−1, the transmission spectra of the sensor exhibit distinct and linear wavelength shift in opposite directions. The maximum sensitivity observed in 0° and 180° bending is 3.252 nm/m−1 and −1.146 nm/m−1, respectively. The maximum temperature sensitivity is 0.0621 nm/°C and this effect can be compensated by using the sensitivity matrix. The advantages of the proposed sensor include its ability to accurately quantify the direction and magnitude of bending, temperature crosstalk elimination, and cost-effectiveness. These features make the sensor highly desirable for a wide range of practical directional bending applications, such as medical wearable devices for human joint range of motion monitoring.

Highly sensitive and easy-to-attach wearable sensor for measuring finger force based on curvature changes in an ellipse-shaped finger ring

Technologies for digitizing worker actions to enhance human labor tasks, mitigate accidents, and prevent disabling injuries have garnered significant attention. This study focuses on monitoring the force exerted by the fingers and developing a wearable fingertip force sensor based on a simple elliptical ring structure in conjunction with a commercially available resistive bend sensor. Resembling a ring accessory, the sensor is easy to attach and detach, and exhibits high sensitivity, with a resistance change of approximately 9% for a fingertip load of 1 N. Furthermore, to mitigate crosstalk during finger flexion, we propose a combined configuration employing this ring-shaped sensor alongside another sensor designed for measuring and rectifying finger flexion angles. Additionally, we introduce an empirically derived fitting function and a straightforward calibration procedure to extract the function’s parameters. The proposed system achieves an average RMS error of 0.53 N for force estimations of approximately 5 N, even during finger flexion and postural changes.

Sawtooth-enhanced bend sensor for gesture recognition

Sawtooth-enhanced bend sensor for gesture recognition

Contact detection in a rib-reinforced vacuum driven actuator with an embedded CSR bridge sensor

ABSTRACT Stable control and accurate sensing are necessary for the safe operation of soft robots, and flexible sensors that can follow the flexible movements of soft robots are needed. In this paper, flexible bending sensors are embedded in a rib-reinforced vacuum-driven actuator with no risk of rupture, considering the application of flexible sensors to human cooperative work and wearable devices. The bending sensor is a bridge circuit with conductive silicone rubber (CSR) for variable resistance. The CSR bridge sensor consists of four CSRs that capture deformation and a circuit with a conductive coating spray printed on a laminated sheet. The sensor value is the output voltage divided by the input voltage, and the effects of the drift and input voltage variations are canceled out. The bending sensor embedded in the silicone actuator has a linear relationship, and the rib-reinforced vacuum driven actuator provides angle PID control for continuously changing reference inputs by feeding back sensor estimated angle. Furthermore, contact detection was established by using the pressure estimated from the CSR bridge sensor and the pressure sensor error as the threshold. The algorithm provides reliable contact detection and force estimation with actuator response thresholds and anti-chattering.

Temperature-insensitive vector curvature sensor based on four-core fiber offset structure

Vector curvature measurement is very important in industrial fields with the unavoidable problem of temperature crosstalk. To improve the detection performance, a novel temperature-insensitive vector bending sensor have been proposed in the paper. The sensor is fabricated by core-offset splicing a segment of four-core fiber (FCF) between two single-mode fibers (SMFs). The Mach-Zehnder interference and the recognition ability of bending directions are enhanced by the core-offset structure. The experimental results demonstrate the curvature sensitivities of our sensor reach 38.16 nm/m−1, 37.46 nm/m−1, 4.99 nm/m−1 and −7.12 nm/m−1 in different bending directions within the range of 0.346–0.49 m−1. The average temperature sensitivity is only −6.67 pm/℃ in the range of 30–90 °C. The all-fiber, low cost, high sensitivity, and excellent vectoriality make our sensor more potential in harsh, complex industrial environment.