Sensors For Soft Robotics Research Articles

Soft Polymer Optical Fiber Sensors for Intelligent Recognition of Elastomer Deformations and Wearable Applications.

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human-machine interaction interfaces.

Ultra‐Stretchable and Environmentally Resilient Hydrogels Via Sugaring‐Out Strategy for Soft Robotics Sensing

AbstractThe adoption of hydrogels in most applications is hampered by their high free water content, which limits their mechanical performance and environmental resilience. Herein, this issue is simultaneously addressed by modulating the state of water and the intermolecular interactions in polyacrylamide (PAM) hydrogels. Specifically, PAM hydrogels are toughened by sugaring‐out using a monosaccharide (glucose, G). Glucose is found to facilitate PAM hydrogen bonding and interchain interactions. Meanwhile, the high hygroscopicity of glucose converts some of the free water to bound state, endowing the hydrogels with remarkable resilience to extreme environmental conditions. The PAM‐G hydrogels are demonstrated as multimodal sensors for soft robotics. Moreover, PAM‐G alcogels produced by solvent exchanging with ethanol are shown as effective opto‐mechanical sensors. Notably, all these properties are obtained by the inclusion of glucose, a green additive showing no negative health and environmental effect.

Development of MWCNT/Magnetite Flexible Triboelectric Sensors by Magnetic Patterning.

The fabrication of low-electrical-percolation-threshold polymer composites aims to reduce the weight fraction of the conductive nanomaterial necessary to achieve a given level of electrical resistivity of the composite. The present work aimed at preparing composites based on multiwalled carbon nanotubes (MWCNTs) and magnetite particles in a polyurethane (PU) matrix to study the effect on the electrical resistance of electrodes produced under magnetic fields. Composites with 1 wt.% of MWCNT, 1 wt.% of magnetite and combinations of both were prepared and analysed. The hybrid composites combined MWCNTs and magnetite at the weight ratios of 1:1; 1:1/6; 1:1/12; and 1:1/24. The results showed that MWCNTs were responsible for the electrical conductivity of the composites since the composites with 1 wt.% magnetite were non-conductive. Combining magnetite particles with MWCNTs reduces the electrical resistance of the composite. SQUID analysis showed that MWCNTs simultaneously exhibit ferromagnetism and diamagnetism, ferromagnetism being dominant at lower magnetic fields and diamagnetism being dominant at higher fields. Conversely, magnetite particles present a ferromagnetic response much stronger than MWCNTs. Finally, optical microscopy (OM) and X-ray micro computed tomography (micro CT) identified the interaction between particles and their location inside the composite. In conclusion, the combination of magnetite and MWCNTs in a polymer composite allows for the control of the location of these particles using an external magnetic field, decreasing the electrical resistance of the electrodes produced. By adding 1 wt.% of magnetite to 1 wt.% of MWCNT (1:1), the electric resistance of the composites decreased from 9 × 104 to 5 × 103 Ω. This approach significantly improved the reproducibility of the electrode's fabrication process, enabling the development of a triboelectric sensor using a polyurethane (PU) composite and silicone rubber (SR). Finally, the method's bearing was demonstrated by developing an automated robotic soft grip with tendon-driven actuation controlled by the triboelectric sensor. The results indicate that magnetic patterning is a versatile and low-cost approach to manufacturing sensors for soft robotics.

Creating Stretchable Electronics from Dual Layer Flex-PCB for Soft Robotic Cardiac Mapping Catheters

The authors present in this study the development of a novel method for creating stretchable electronics from dual-layer flex printed circuit boards (flex-PCBs) as a platform for soft robotic sensor arrays (SRSAs) for cardiac voltage mapping applications. There is a crucial need for devices that utilize multiple sensors and provide high performance signal acquisition for cardiac mapping. Previously, our group demonstrated how single-layer flex-PCB can be postprocessed to create a stretchable electronic sensing array. In this work, a detailed fabrication process for creating a dual-layer multielectrode flex-PCB SRSA is presented, along with relevant parameters to achieve optimal postprocessing with a laser cutter. The dual-layer flex-PCB SRSA’s ability to acquire electrical signals is demonstrated both in vitro as well as in vivo on a Leporine cardiac surface. These SRSAs could be extended into full-chamber cardiac mapping catheter applications. Our results show a significant contribution towards the scalable use of dual-layer flex-PCB for stretchable electronics.

Fabrication of Low Electrical Percolation Threshold Multi-Walled Carbon Nanotube Sensors Using Magnetic Patterning

Soft robotics is an expanding area with multiple applications; however, building low-cost, soft, and flexible robots requires the development of sensors that can be directly integrated into the soft robotics fabrication process. Thus, the motivation for this work was the design of a low-cost fabrication process of flexible sensors that can detect touch and deformation. The fabrication process proposed uses a flexible polymer nanocomposite with permanent magnets strategically placed where the conductive electrodes should be. The nanocomposite is based on poly(dimethylsiloxane) (PDMS) and multi-walled carbon nanotubes (MWCNTs). The MWCNT contains ferromagnetic impurities remaining from the synthesis process, which can be used for magnetic manipulation. Several electrode geometries were successfully simulated and tested. The magnetic patterning was simulated, allowing the fabrication of conductive patterns within the composite. This fabrication process allowed the reduction of the electrical resistivity of the nanocomposites as compared to the composites with homogeneous MWCNT dispersion. It also allowed the fabrication of piezoresistive and triboelectric sensors at MWCNT concentration as low as 0.5 wt.%. The fabrication process proposed is flexible, allows the development of sensors for soft robotics, as well as monitoring large and unconventional areas, and may be adapted to different mould shapes and polymers at low cost.

Flexible and Stretchable Carbon-Based Sensors and Actuators for Soft Robots.

In recent years, the emergence of low-dimensional carbon-based materials, such as carbon dots, carbon nanotubes, and graphene, together with the advances in materials science, have greatly enriched the variety of flexible and stretchable electronic devices. Compared with conventional rigid devices, these soft robotic sensors and actuators exhibit remarkable advantages in terms of their biocompatibility, portability, power efficiency, and wearability, thus creating myriad possibilities of novel wearable and implantable tactile sensors, as well as micro-/nano-soft actuation systems. Interestingly, not only are carbon-based materials ideal constituents for photodetectors, gas, thermal, triboelectric sensors due to their geometry and extraordinary sensitivity to various external stimuli, but they also provide significantly more precise manipulation of the actuators than conventional centimeter-scale pneumatic and hydraulic robotic actuators, at a molecular level. In this review, we summarize recent progress on state-of-the-art flexible and stretchable carbon-based sensors and actuators that have creatively added to the development of biomedicine, nanoscience, materials science, as well as soft robotics. In the end, we propose the future potential of carbon-based materials for biomedical and soft robotic applications.

A review: Machine learning for strain sensor-integrated soft robots

Compliant and soft sensors that detect machinal deformations become prevalent in emerging soft robots for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy body of the soft robot enables programmable actuating behaviors and automated manipulations across a wide strain range, which poses high requirements for the integrated sensors of customized sensor characteristics, high-throughput data processing, and timely decision-making. As various soft robotic sensors (strain, pressure, shear, etc.) meet similar challenges, in this perspective, we choose strain sensor as a representative example and summarize the latest advancement of strain sensor-integrated soft robotic design driven by machine learning techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. These machine learning implementations greatly accelerate robot automation, reduce resource consumption, and expand the working scenarios of soft robots. We also discuss the prospects of fusing machine learning and soft sensing technology for creating next-generation intelligent soft robots.

Vision-Based Sensing for Electrically-Driven Soft Actuators

Developing reliable control strategies in soft robotics requires advances in soft robot perception. However, current soft robotic sensors pose many performance limitations, and available materials and manufacturing techniques complicate soft sensorized robot design. To address these long-standing needs, we introduce a method for using vision to sensorize robust, electrically-driven soft robotic actuators constructed from a new class of architected materials. Specifically, we use cameras positioned within the hollow interiors of handed shearing auxetic (HSA) actuators to record deformation during motion. We train a convolutional neural network (CNN) that maps the visual feedback to the actuator's tip pose. Our model provides predictions with sub-millimeter accuracy from only six minutes of training data, while remaining lightweight with an inference time of 18 milliseconds per frame. We also develop a model that additionally predicts the horizontal tip force acting on the actuator and generalizes to previously unseen forces. Finally, we demonstrate the viability of our sensorization strategy for contact-rich applications by training a CNN that predicts the tip pose accurately during tactile interactions. Overall, our methods present a reliable vision-based approach for designing sensorized soft robots built from electrically-actuated, architected materials.

Strain and temperature sensors made from fishing lines

Soft and flexible sensors are demanded in a wide variety of wearables and soft-robotic applications. We designed and developed novel integrated strain and temperature sensors using super-coiled transparent polymer fibres as an optical waveguide by exploiting changes in macro-bending optical losses induced by strain or heat. Polymers used in this research such as nylon and fluorocarbon are shaped as super-coils. The customized optical couplings were designed that can sustain tension induced by axial strain force or thermal stress (entropic spring effect). A customized method of transmission and measurement of the optical signal transferred through a super-coiled polymeric waveguide was designed to reduce attenuation losses. Transmitted optical signals were recorded in the range of −65 dBm to −45 dBm. Changes in the measured transmitted optical signal were recorded and compared for transparent mono-filament nylon fibre fishing line and fluorocarbon fibre fishing line. The trends of the measured parameters registered during the testing period showed a variation of ∼2%, good linearity and repeatability, however with minor hysteresis in the range of for nylon and the range of for fluorocarbon. Limited optical characteristics of these materials were compensated and substituted by high sensitivity introduced by a small macro-bend radius. These sensing devices combine high sensitivity with large comparative strain and a wide range of temperatures. Polymers used in this research exhibit the potential for use in SCP waveguide-based soft robotic sensors. Measured reaction times validate the potential of these sensors in measurements of moderate or slow speed processes. These sensing devices also combine a simple manufacturing process that uses abundant and cost-effective materials with a wide array of potential applications.

A Triboelectric Joint Sensor Imitating Soft Robot for Human Joint Rehabilitation Monitoring

Human bone joint monitoring is essential for medical treatment, especially in the recovery phase of injury. It is worth noting that bending sensors based on soft robots have developed rapidly in recent years. Due to their flexibility, soft robots have a limitless number of degrees of freedom, but they also confront severe sensing and control issues. Meanwhile, triboelectric nanogenerators (TENGs) are projected to be self-powered sensors for soft robotics owing to their great sensitivity, versatility and simplicity. In this work, we first propose a triboelectric joint sensor (TJS) based on the TENG device. The TJS can provide the response on the dynamic, and the TJS’s output voltage is proportional to the local bending angle. According to the results, the voltage of TJS device rises linearly from 3.6[Formula: see text]V to 45[Formula: see text]V as the bending angle increases from 0∘ to 60∘. Furthermore, the TJS device can be used to monitor the postoperative rehabilitation of hand joints through the voltage signal of TJS device installed on the finger. This study will promote the application of triboelectric sensor simulating soft robot device in the field of medical monitoring.

Integration of deep learning and soft robotics for a biomimetic approach to nonlinear sensing

Traditional approaches to sensing have often been aimed at simple sensor characteristics to make interpretation of the sensor outputs easier, but this has also limited the quality of the encoded sensory information. Integrating a complex sensor with deep learning could hence be a strategy for removing current limitations on the information that sensory inputs can carry. Here, we demonstrate this concept with a soft-robotic sensor that mimics fast non-rigid deformation of the ears in certain bat species. We show that a deep convolutional neural network can use the nonlinear Doppler shift signatures generated by these motions to estimate the direction of a sound source with an estimation error of ~0.5°. Previously, determining the direction of a sound source based on pressure receivers required either multiple frequencies or multiple receivers. Our current results demonstrate a third approach that makes do with only a single frequency and a single receiver. Bats with sophisticated biosonar systems move their ears at a high speed to help localize sound sources. Yin and Muller present a system inspired by this strategy, which can localize sounds with high accuracy and with a single detector, using a flexible silicone model of a bat’s ear and a deep convolutional neural network to process the complex Doppler signatures.

Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation

Soft robotic sensors have been limited in their applications due to their highly nonlinear time variant behavior. Current studies are either looking into techniques to improve the mechano-electrical properties of these sensors or into modelling algorithms that account for the history of each sensor. Here, we present a method for combining multi-material soft strain sensors to obtain equivalent higher quality sensors; better than each of the individual strain sensors. The core idea behind this work is to use a combination of redundant and disjoint strain sensors to compensate for the time-variant hidden states of a soft-bodied system, to finally obtain the true strain state in a static manner using a learning-based approach. We provide methods to develop these variable sensors and metrics to estimate their dissimilarity and efficacy of each sensor combinations, which can double down as a benchmarking tool for soft robotic sensors. The proposed approach is experimentally validated on a pneumatic actuator with embedded soft strain sensors. Our results show that static data from a combination of nonlinear time variant strain sensors is sufficient to accurately estimate the strain state of a system.

Three-dimensional printing of tactile sensors for soft robotics

Three-dimensional (3D) printing has become an important fabrication method for soft robotics, due to its ability to make complex 3D structures from computer designs in simple steps and multimaterial co-deposition ability. In this article, the application of 3D printing techniques in the fabrication of four types of tactile sensors commonly used in soft robotics, including the piezoresistive tactile sensor, capacitive tactile sensor, piezoelectric tactile sensor, and triboelectric tactile sensor, will be discussed. The 3D printing mechanism, material, and structure for each type of sensor will be introduced, and the perspectives on the future potential of 3D printable tactile sensors will be discussed.

Modeling actuation and sensing in ionic polymer metal composites by electrochemo-poromechanics

Ionic polymer metal composites (IPMCs) consist of an electroactive polymeric membrane plated with metal electrodes. They hold promise as actuators and sensors for soft robotics and biomedical applications. Their capabilities ensue from the motion, within the membrane, of a fluid phase consisting of ions dispersed in a solvent. Toward a thorough understanding of IPMC multiphysics, we propose a large deformation theory combining electrochemistry and poromechanics. Namely, we modify the theory recently developed by Porfiri’s group by introducing the transport of the solvent, whose redistribution determines the volumetric deformation of the fluid-saturated membrane, and we further account for the cross-diffusion of solvent and ions. In actuation, the imposed voltage drop across the electrodes triggers ion migration, such that the solvent is transported toward the cathode by electro-osmosis. This determines the initial bending toward the anode; then, back-relaxation occurs because of both the solvent counter-diffusion and the asymmetric redistribution of ions near the electrodes. In short-circuit sensing, the applied load triggers solvent motion, such that ions are mainly transported toward the cathode by convection with the solvent. This determines charge accumulation; then, ion counter-diffusion leads to a decrease of the charge stored at the electrodes. We demonstrate that these behaviors can be predicted by the proposed theory on the basis of relevant finite element benchmarks. Additionally, our analysis encompasses the assessment of the role of the membrane elastic moduli in the counter-diffusion of solvent and ions in IPMC actuation and sensing.

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

Soft robotic sensors, a class of pliable, embeddable sensors, are well-suited for applications in wearable technology because of their ease of integration with common clothing articles. The suitability of soft robotic sensors for estimation of human joint angles has been proven; this research represents another step towards development of a reliable laboratory data collection platform for the human ankle joint complex. In this research, the accuracy and repeatability of a newly-developed wearable prototype are evaluated as a potential replacement for camera-based motion capture by measuring differences between simultaneously collected motion capture and stretch sensor data. The accuracy of these measurements is compared to measurements collected using a previous prototype for validation. Results show that the newly-developed prototype is capable of joint angle estimation within 1.86° mean-absolute-error during complex, dynamic movements and that wearing shoes over the sock prototype does not significantly degrade performance.

Highly Stretchable Polymer Optical Fiber for Mechanical Sensing in Artificial Tendons: Towards Novel Sensors for Soft Robotics

The control of tendon-driven actuators is mainly affected by the tendon behavior under stress or strain. The measurement of these parameters on artificial tendons brings benefits on the control and novel approaches for soft robotics actuators. This paper presents the development of polymer optical fiber sensors fabricated through the light spinning polymerization process (LPS-POF) in artificial tendons. This fiber has exceptionally low Young’s modulus and high strain limits, suitable for sensing applications in soft structures. Two different configurations are tested, indicating the possibility of measuring strain and stress applied in the tendon with determination coefficients of 0.996 and 0.994, respectively.

Multilayer fabrication of durable catheter-deployable soft robotic sensor arrays for efficient left atrial mapping.

Devices that perform cardiac mapping and ablation to treat atrial fibrillation provide an effective means of treatment. Current devices, however, have limitations that either require tedious point-by-point mapping of a cardiac chamber or have limited ability to conform to the complex anatomy of a patient's cardiac chamber. In this work, a detailed, scalable, and manufacturable technique is reported for fabrication of a multielectrode, soft robotic sensor array. These devices exhibit high conformability (~85 to 90%) and are equipped with an array of stretchable electronic sensors for voltage mapping. The form factor of the device is intended to match that of the entire left atrium and has a hydraulically actuated soft robotic structure whose profile facilitates deployment from a 13.5-Fr catheter. We anticipate that the methods described in this paper will serve a new generation of conformable medical devices that leverage the unique characteristics of stretchable electronics and soft robotics.

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

Joint Entropy-Based Morphology Optimization of Soft Strain Sensor Networks for Functional Robustness

Dense and distributed tactile sensors are critical for robots to achieve human-like manipulation skills. Soft robotic sensors are a potential technological solution to obtain the required high dimensional sensory information unobtrusively. However, the design of this new class of sensors is still based on human intuition or derived from traditional flex sensors. This work is a first step towards automated design of soft sensor morphologies based on optimization of information theory metrics and machine learning. Elementary simulation models are used to develop the optimized sensor morphologies that are more accurate and robust with the same number of sensors. Same configurations are replicated experimentally to validate the feasibility of such an approach for practical applications. Furthermore, we present a novel technique for drift compensation in soft strain sensors that allows us to obtain accurate contact localization. This work is an effort towards transferring the paradigm of morphological computation from soft actuator designing to soft sensor designing for high performance, resilient tactile sensory networks.

Closing the Wearable Gap-Part VII: A Retrospective of Stretch Sensor Tool Kit Development for Benchmark Testing

This paper presents a retrospective of the benchmark testing methodologies developed and accumulated into the stretch sensor tool kit (SSTK) by the research team during the Closing the Wearable Gap series of studies. The techniques developed to validate stretchable soft robotic sensors (SRS) as a means for collecting human kinetic and kinematic data at the foot-ankle complex and at the wrist are reviewed. Lessons learned from past experiments are addressed, as well as what comprises the current SSTK based on what the researchers learned over the course of multiple studies. Three core components of the SSTK are featured: (a) material testing tools, (b) data analysis software, and (c) data collection devices. Results collected indicate that the stretch sensors are a viable means for predicting kinematic data based on the most recent gait analysis study conducted by the researchers (average root mean squared error or RMSE = 3.63°). With the aid of SSTK defined in this study summary and shared with the academic community on GitHub, researchers will be able to undergo more rigorous validation methodologies of SRS validation. A summary of the current state of the SSTK is detailed and includes insight into upcoming experiments that will utilize more sophisticated techniques for fatigue testing and gait analysis, utilizing SRS as the data collection solution.