AbstractMotile cilia move in an asymmetric pattern and implement a metachronal wave (MCW) to facilitate fluid movement in a viscous environment. Studies have been conducted to mimic MCW movement of motile cilia, but the fabrication process was too complicating or there were difficulties in accurately mimicking the shape of the cilia. To overcome these limitations, we introduce a self-assembly method to fabricate a reprogrammable magnetically actuated self-assembled (RMS) cilia array that can be reprogrammed by changing the magnetization direction through additional magnetization. Using the RMS cilia array, a unilateral cilia array (UCA) channel and a bilateral cilia array (BCA) channel were constructed, and the motion and fluid flow of the RMS cilia array were analyzed by applying different magnetic fields (strike magnetic field and rotating magnetic field). When a rotating magnetic field was applied to the UCA channel, a distinct MCW appeared. In the BCA channel test, fluid pumping was observed when a strike magnetic field, whereas fluid mixing was observed when a rotating magnetic field was applied. Based on these results, it is expected that the proposed RMS cilia array and magnetic field actuation method can be applied to lab-on-a-chip or microfluidic channels for fluid mixing and pumping.1. IntroductionCilia are hair-like, microtubule-based structures that have various distributions with a length of approximately 3–200 µm and an aspect ratio ranging from 10 to 100, depending on the location where they are found, and are divided into primary cilia and motile cilia.[1–5] Motile cilia can move objects or mix fluids by moving mucus or body fluids in the human body.[6,7] Among these motile cilia, the cilia that are found in the fallopian tube of the female reproductive system help the movement of the ovary, and the cilia existing in the lungs mix settled dust and bacteria through mucociliary clearance and move them out of the body.[8–10] The environment in which motile cilia move is normally filled with fluid with a low Reynolds number. In such an environment, the viscous force is generally more dominant than the inertial force, and has a significant influence on the fluid flow.[11–13] To be helpful in this environment, the cilium moves in an asymmetrical pattern comprising an effective stroke and a recovery stroke, creating a net fluid flow. In an effective stroke, the cilium moves in an arc that is fully stretched, while in the recovery stroke, the cilium returns to the starting point in a bent state as if swinging, which increases the moving area of the cilium.[14–16] In addition, when several cilia gather to form a cilia array, they move in a sequential pattern that forms a wave called the metachronal wave (MCW), which helps move the fluid faster and more efficiently because of their asymmetrical motion.[17–20]Several studies have reported mimicking the asymmetric motion of cilia and the MCW motion to efficiently pump or mix fluids in microfluidic devices with low Reynolds numbers.[21–23] To mimic cilia motion, many actuation methods have been used; actuation via a magnetic field is the most used method among them.[24–27] In addition, diverse manufacturing methods exist for magnetically actuated artificial cilia, and, the fabrication method using self-assembly has the advantages of simplicity and capability to mimic the appearance of natural cilia. However, it is difficult to program the magnetization direction, thus limiting the implementation of the MCW of the cilia.[28–31]Nevertheless, studies have been conducted to imitate the metachronal wave of cilia by fabricating artificial cilia using the molding method, which facilitates reprogramming.[32,33] Nelson formed a cilia array using a molding method and then reprogrammed the cilia array, which moved the cilia array to form an MCW. Sitti fabricated micro-cilia using a mold, magnetized each cilium independently, and attached them to form a cilia array with the desired arrangement that implements the MCW. However, these studies actuated the cilia array using only a rotating external magnetic field, and because the cilia array was fabricated using the molding method, several complex steps were required for making the cilia array.
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: _ [email protected] _
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.
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: [email protected]
Being minimally invasive and highly effective, radiofrequency ablation (RFA) is widely used for small size malignant tumors treatment. However, in clinical practice, a large number of tumors are found in irregular shape, while the current RFA devices are hard to control their morphologic appearance of RFA lesions on demand, which usually ends up with unnecessarily excessive tissue ablation and subsequently often brings irreversible damage to the organs’ functions. Here, we introduce active cannulas for each of individually-controlled sub-electrodes to achieve an on-demand shape morphing and thus conformal RFA lesion. The target shape as well as the length of inserted sub-electrodes can be precisely controlled by tuning the active stylets and cannulas. What’s more, owing to independent movement and energy control of each sub-electrodes, our electrode is shown to be not only efficient enough to accomplish accurate trajectory control to target tissue in a single insertion, but also adaptive enough to ablate target tissues with diverse morphologic appearances and locations. On-demand conformal ablation of target tissue is demonstrated as well under the guidance of ultrasound imaging with our device. Potentially, our RFA electrode is a promising minimally invasive treatments of malignant tumors in the future clinical practice. Corresponding author(s) Email: [email protected]
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.
AbstractFabrication of structures in unstructured environments is a promising field to expand the application spaces of additive manufacturing (AM). One potential application is to add new components directly onto existing structures. In this paper, we developed a versatile, reconfigurable direct ink write (DIW) manufacturing method in tandem with a two-stage hybrid ink designed to fabricate high-strength, self-supporting parts in unconventional printing spaces, such as underneath a build surface or horizontally. Our two-stage hybrid DIW ink combines a photopolymer and a tough epoxy resin. The photopolymer can be cured rapidly to enable layer-by-layer printing complex structures. It also possesses adequate adhesion to allow the fabrication of large volume structures on a diversity of substrates including acrylic, wood, glass, aluminum, and concrete. The epoxy component can be cured after 72 hours in ambient conditions with further increased adhesion strengths. We demonstrated the capabilities of the reconfigurable DIW extrusion nozzle method to print complex structures in inverted and horizontal environments. Finally, via the addition of DIW-deposited conductive paths, we created a functional 3D printed structure capable of in-situ deformation monitoring. This work has the potential to be used for applications such as appending new parts to existing structures for increasing functionality, repair, and structure health monitoring.Corresponding author: H. Jerry Qi, [email protected]
The cocktail party problem refers to a challenging process when the human sensory system tries to separate a specific voice from a loud mixture of background sound sources. The problem is much more demanding for machines and has become the holy grail in robotic hearing. Despite the many advances in noise suppression, the intrinsic information from the contaminated acoustic channel remains difficult to recover. Here we show a simple-yet-powerful laser-assisted audio system termed REAL (Robot Ear Accomplished by Laser) to probe the vibrations of sound-carrying surfaces (mask, throat and other nearby surfaces) in optical channel, which is intrinsically immune to acoustic background noises. Our results demonstrate that REAL can directly obtain the audio-frequency content from the laser without acoustic channel interference. The signals can be further transcribed into human-recognizable audio by exploiting the internal time and frequency correlations through memory-enabled neural networks. The REAL system would enable a new way in human-robot interaction. Xiaoping Hong Email: [email protected]
Two-dimensional metal-organic frameworks (2D-MOFs) have been extensively studied as promising materials in the fields of eletrocatalysis, drug delivery, electronic devicese, etc. However, few studies have explored the application potential of 2D-MOFs in novel neuromorphic computing devices. In this work, we report an optoelectronic neuromorphic transistor based on a 2D-MOFs/polymer charge-trapping layer. We found that, the large specific surface area, stable crystal structure, and highly accessible active sites in 2D-MOFs make them excellent charge-trapping materials for our devices, which are beneficial for mimicking the memory and learning functions observed in the organism's nervous systems. Different types of synaptic behaviors have been realized in our 2D-MOFs-based neuromorphic devices under stimuli signal, e.g., paired-pulse facilitation, excitatory post-synaptic current, short-term memory, and long-term memory. More interestingly, emotion-adjustable learning behavior was realized by changing the value of the source-drain voltage. This work can shed light on the application of 2D-MOFs in neuromorphic computing and will contribute to the further development of neuromorphic computing devices. Corresponding authors Email: [email protected] (Jia Huang) [email protected] (Shilei Dai)
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.
This Supporting Information includes: _Supplementary text describing Preliminary Status Classifer, segmentation methods, model training and validation details; two supplementary tables, two supplementary figures and one supplementary video._ Corresponding author(s) Email: _ [email protected]; [email protected]; [email protected] _