Conventional vision systems suffer from lots of data handling between memory and processing units. Inspired by how humans recognize noisy images and the flexible modulation on the timescale of ion dynamics inside an emerging memtransistor, we report a novel neuromorphic vision system based on the ion-modulated memtransistors. By controlling the ion doping processes under adequate stimuli strengths, both short-term and long-term ion dynamics can be utilized to deliver energy-efficient data processing. When dealing with image reconstructions, the short-term accumulation effect of the device can help filter noises in a set of received noisy images while enhancing the original pattern information. The increased contrast can help distinguish the actual contents. To demonstrate systematic performances with the reconfiguration of devices, we extract the nonlinear relationship between channel conductance variation and the amplitude of gate pulses into the network-level simulation. Also, with the nonvolatile conductance change characteristic, the task of recognizing noisy images is performed to verify the versatility of ion-modulated memtransistors in the neuromorphic artificial vision systems.
Manipulation strategies based on the passive dynamics of soft-bodied interactions provide robust performances with limited sensory information. They utilise the kinematic structure and passive dynamics of the body to adapt to objects of varying shapes and properties. However, these soft passive interactions make the state of the robotic device influenced by the environment, making control generation and state estimation difficult. This work presents a closed-loop framework for dynamic interaction-based grasping that relies on two novelties: (i) a wrist-driven passive soft anthropomorphic hand that can generate robust grasp strategies using one-step kinaesthetic teaching and (ii) a learning-based perception system that uses temporal data from sparse tactile sensors to predict and adapt to failures before it happens. With the anthropomorphic soft design and wrist-driven control, we show that controllers can be generated robust to novel objects and location uncertainty. With the learning-based high-level perception system and 32 sensing receptors, we show that failures can be predicted in advance, further improving the robustness of the entire system by more than doubling the grasping success rate. From over 1000 real-world grasping trials, both the control and perception framework are also seen to be transferable to novel objects and conditions. Corresponding author(s) Email: _ firstname.lastname@example.org _
In this study, we utilize simple light-emitting diodes (LEDs) and photodetectors (PDs) combined with an intelligent shape decoding framework to enable 3D shape sensing of a self-contained flexible substrate. Finite element analysis (FEA) is leveraged to optimize the LED-PD layout and enrich ground-truth data from sparse to dense points for model training. The mapping from light intensities to overall sensor shape was achieved with an autoregression-based model that considers temporal continuity and spatial locality. The sensing framework was evaluated on an A5-sized flexible sensor prototype and a fish-shaped prototype, where sensing accuracy (RMSE = 0.27 mm) and repeatability (Δ light intensity < 0.31% over 1000 cycles) were tested underwater. We validate an affordable alternative to FBG sensors with high-order sensing outputs, where demonstrations are supplemented in the below videos.
Aerial robots can autonomously collect temporal and spatial high-resolution environmental data. This data can then be utilized to develop mathematical ecology models to understand the impact of climate change on our habitat. In case of the drone's malfunction the incorporated materials can threaten vulnerable environments. The recent introduction of transient robotics has enabled the development of biodegradable, environmental sensing drones capable of degrading in their environment. However, manufacturing methods for environmental sensing transient drones are rarely discussed. In this work, we highlight a manufacturing framework and material selection process featuring biopolymer-based, high-strength composite cryogels and printed carbon-based electronics for transient drones. We found that gelatin and cellulose based cryogels mechanically outperform other biopolymer composites while having a homogeneous micro-structure and high stiffness-to-weight ratio. The selected materials are used to manufacture a flying-wing air-frame, while the incorporated sensing skin is capable of measuring the elevons' deflection angles as well as ambient temperature. Our results demonstrate how gelatin-cellulose cryogels can be used to manufacture lightweight transient drones while printing carbon conductive electronics is a viable method for designing sustainable, integrated sensors. The proposed methods can be used to guide the development of lightweight and rapidly degrading robots, featuring eco-friendly sensing capabilities.Corresponding author(s) Email: email@example.com, firstname.lastname@example.org
Artificial muscles with large strokes are of special interest in diverse fields. However, it is difficult for large-diameter muscles to be rapidly cycled. In this study, hair artificial muscles with extremely large tensile stroke and fast recovery were prepared simply by twist insertion, coiling and steaming. The maximum tensile stroke for the hair artificial muscles upon water actuation was as large as 10000% and the large-stroke muscles could recover fast in ethanol. With a diameter of 7 mm and a twist density of 2500 turns m-1, the compacted heterochiral hair artificial muscle could elongate 100 times of its original length in water and returned to its initial length in ethanol within 10 s. In addition, these hair artificial muscles maintained their excellent performance after either 100 water-ethanol stimulation cycles or staying in open air for 5 months. Moreover, the hair artificial muscle was able to contract by 59% when lifting 10 times its own weight, pull a wheel model or climb a long distance under water and work as a smart water-sensitive switch. This work demonstrates a facile and green strategy to prepare advanced natural fiber-based artificial muscles that have promising applications in soft robotics and biomedical engineering. Corresponding author(s) Email: email@example.com (Dr. Si Sun), firstname.lastname@example.org (Dr. Xiao-Li Qiang), & email@example.com (Dr. Xiao-Long Shi)
The temperature has a large impact on the rate of a chemical reaction. For photoelectrochemical water splitting it has been shown that the photocurrent of a tungsten oxide anode increases by 64 % in a temperature interval of 25 to 65 °C. Photoelectrochemical cells are usually not equipped with systems for active temperature control. This limits the reliability of measurement data, especially for long measurements under illumination (e.g., impedance spectroscopy). Insufficient comparability of materials is an obstacle for development and application of photoelectrochemical modules.
The theoretical capability of modular robot to organize the overall robot into different structures with different functions has broad prospects in space exploration. Therefore, we develop a novel modular space robot named Space Module, and inspired by biological cooperative and mutual assistance behaviors, a novel self-assembly method is proposed for it.To solve the mobility problem of non-mobile modules, a new meta-modules design for Space Module is presented, based on which the concept of mutual assistance is utilized to achieve position and posture reachability of assembled unit while minimizing the effect of meta-modules on granularity. Then, an assembly planner is designed to obtain the assembly sequences according to the unique motion characteristics of meta-module and mutual assistance to realize the self-manufacturing of desired configurations. Finally, several demonstrations are given to verify the validity and feasibility of the proposed assembly method.Corresponding author(s) Email: firstname.lastname@example.org
With advancements in automation and high-throughput techniques, complex materials discovery with multiple conflicting objectives can now be tackled in experimental labs. Given that physical experimentation is greatly limited by evaluation budget, maximizing efficiency of optimization becomes crucial. We discuss the limitations of using hypervolume as a performance indicator for desired optimality across the entire multi-objective optimization run and propose new metrics specific to experimentation: ability to perform well for complex high-dimensional problems, minimizing wastage of evaluations, consistency/robustness of optimization, and ability to scale well to high throughputs. With these metrics, we perform a comparison of two conceptually different and state-of-the-art algorithms (Bayesian and Evolutionary) on synthetic and real-world datasets. We discuss the merits of both approaches with respect to exploration and exploitation, where fully resolving the Pareto Front could be the main aim for greater scientific value in understanding materials space, and thus provide a perspective for materials scientists to implement optimization in their platforms.
The metaverse, where the virtual and real world are fused, is currently under rapid development. Immersive and vivid experience in the metaverse requires human-machine interaction devices that, unlike those currently available, are simultaneously imperceptible, convenient to use, inexpensive, and safe. In this study, we propose and realize an optical-nanofiber-based gesture-recognition wristband that can accurately recognize gestures and use them to interact with a robotic hand. Requiring only three optical-nanofiber-based pressure sensors, the wristband is simple in structure, convenient to use, and remarkably imperceptible to the user. With the assistance of a machine-learning algorithm, a maximum recognition accuracy of 94% is achieved for testers with different physiques. A robotic hand can be remotely controlled by the wristband through gestures. The wristband has broad application prospects and is a promising solution for advanced human-machine-interaction devices.
AbstractIn recent years, there has been a growing interest in the development of universal soft grippers that can handle objects of varying form factors (including flat objects), surface condition (including moistened or oily objects), and mechanical properties (deformable and fragile). Yet, there is no single gripper that can gently grip objects with such a wide range of properties. In this paper, we present a soft gripper that combines granular jamming (GJ) and electroadhesion (EA) to gently grasp and release a large set of diverse objects. The gripper can operate in GJ mode only, in EA mode only, or in a combination mode that simultaneously activates GJ and EA. In GJ mode, the gripper can grasp objects with different surface properties, lift objects 38 times its own weight using negative pressure, and release objects by applying positive pressure, but has difficulty in handling flat and fragile objects. In EA mode, the gripper can manipulate flat and fragile objects but encounters difficulties with different surface properties such as oily or moistened. In the combination mode, the gripper can generates grasping forces up to 35% higher than in the GJ mode for all object sizes and certain shapes such as a cylinder.IntroductionThe softness of the human hand is a critical factor that allows us to hold, lift, and manipulate a variety of objects and has inspired roboticists to incorporate softness in gripper design and materials. The compliance of soft materials enables passive adaptation of the gripper during grasping operations allowing manipulation of a wide range of objects without bringing additional control complexities.[1,2] In recent years, there has been a growing interest in the development of universal soft grippers that can work with objects of different form factors, rigidity, surface properties, and level of fragility.[3–6] A possible approach to create such highly versatile grippers is to combine different gripping technologies that complement their individual limitations.[1,3,7–10] Yet, it is still challenging to develop a single gripper that can grasp and release objects of different form factor including flat objects, surface conditions (wet, porous, oily, and powdered), and mechanical properties (fragile and deformable).In this paper, we present a soft gripper capable of manipulating different objects with varying physical properties, such as shape, surface conditions, and rigidity. The proposed gripper combines two different technologies: granular jamming to control stiffness and electroadhesion to control adhesion. Here we show that not only does this combination mutually compensate for the limitations of each individual technology, but it also makes the gripper capable of performing multi-stage grasping tasks that consist of diverse grasping and releasing operations on objects made of different material, surface, and shape. The manipulation of a book is an example of multi-stage operation that requires grasping and turning a rigid cover and flipping through single pages.Granular jamming (GJ) enables reversible stiffness change between soft and rigid configurations by means of negative pressure[5,11,12]. High compliance in the soft state allows a GJ gripper to envelope the manipulated object by pressing on it. When negative pressure is applied, the gripper becomes stiff and holds the encaged object. Variable stiffness can also be achieved by integrating phase-change materials that vary mechanical properties under thermal stimulation.[7,13] However, granular jamming offers comparatively faster response time (~100ms), independence from environmental temperature, higher lifting force, easier fabrication, higher robustness, and lower cost.[2,5,14,15] The grasping force produced by granular jamming is sufficient to grasp objects of different morphologies, almost independently of the surface conditions of the object.[5,11,16] The grasping force of GJ grippers can vary from 0.09 to 1.2 kN. GJ has been combined with soft pneumatic actuators to provide more dexterous grasp and lift heavier objects because of the enhanced holding forces.[10,17] However, GJ grippers cannot lift flat objects, such as a sheet of paper. Also, the grasping performance of delicate, fragile and easily deformable objects such as a thin layer of cloth, an egg, or water balloons, as well as larger objects than the active area of the granular bag can be challenging and have not been demonstrated so far.Electroadhesion (EA) instead is an adhesive technology that leverages the shear force generated by electrostatic forces. Electroadhesive pads have been combined with different actuation technologies, such as dielectric elastomer actuation, soft pneumatic actuation, layer jamming, and Fin-Ray structured actuation. The enhanced shear force makes EA-based grippers capable of delicately grasping both flat and fragile objects without squeezing or breaking them.[1,22–26] While the adhesive force of EA pads can be tuned by regulating electrical input, EA effectiveness is highly dependent on the environmental and surface conditions of the object being grasped. In particular electroadhesion is less effective for objects that are greasy, rough, or wet. An additional challenge of soft grippers that rely on electroadhesion is the residual electrostatic charge that remains for a few seconds after removing the voltage and can result in difficult release of light objects.
Multi-chamber soft pneumatic actuators (m-SPAs) have been widely used in soft robotic systems to achieve versatile grasping and locomotion. However, existing m-SPAs have slow actuation speed and are either limited by a finite air supply or require energy-consuming hardware to continuously supply compressed air. Here, we address these shortcomings by introducing an internal exhaust air recirculation (IEAR) mechanism for high-speed and low-energy actuation of m-SPAs. This mechanism recirculates the exhaust compressed air and recovers the energy by harnessing the rhythmic actuation of multiple chambers. We develop a theoretical model to guide the analysis of the IEAR mechanism, which agrees well with the experimental results. Comparative experimental results of several sets of m-SPAs show that our IEAR mechanism significantly improves the actuation speed by more than 82.4% and reduces the energy consumption per cycle by more than 47.7% under typical conditions. We further demonstrate the promising applications of the IEAR mechanism in various pneumatic soft machines and robots such as a robotic fin, fabric-based finger, and quadruped robot. Corresponding author(s) Email: email@example.com
Atomic force microscopy (AFM) is routinely used as a metrological tool among diverse scientific and engineering disciplines. A typical AFM, however, is intrinsically limited by low throughput and is inoperable under extreme conditions. Thus, this work attempts to provide an alternative with a conventional optical microscope (OM) by training a deep learning model to predict surface topography from surface OM images. The feasibility of our novel methodology is shown with germanium-on-nothing (GON) samples, which are self-assembled structures that undergo surface and sub-surface morphological transformations upon high-temperature annealing. Their transformed surface topographies are predicted based on OM-AFM correlation of 3 different surfaces, bearing an error of about 15% with 1.72× resolution upscale from OM to AFM. The OM-based approach brings about significant improvement in topography measurement throughput (equivalent to OM acquisition rate, up to 200 frames per second) and area (∼1 mm²). Furthermore, this method is operable even under extreme environments when an _in-situ_ measurement is impossible. Based on such competence, we also demonstrate the model’s simultaneous application in further specimen analysis, namely surface morphological classification and simulation of dynamic surfaces’ transformation.