One-step formation of polymorphous sperm-like microswimmers by vortex turbulence-assisted microfluidics.

Microswimmers are considered promising candidates for active cargo delivery to benefit a wide spectrum of biomedical applications. Yet, big challenges still remain in designing the microswimmers with effective propelling, desirable loading and adaptive releasing abilities all in one. Inspired by the morphology and biofunction of spermatozoa, we report a one-step formation strategy of polymorphous sperm-like magnetic microswimmers (PSMs) by developing a vortex turbulence-assisted microfluidics (VTAM) platform. The fabricated PSM is biodegradable with a core-shell head and flexible tail, and their morphology can be adjusted by vortex flow rotation speed and calcium chloride solution concentration. Benefiting from the sperm-like design, our PSM exhibits both effective motion ability under remote mag/netic actuation and protective encapsulation ability for material loading. Further, it can also realize the stable sustain release after alginate-chitosan-alginate (ACA) layer coating modification. This research proposes and verifies a new strategy for the sperm-like microswimmer construction, offering an alternative solution for the target delivery of diverse drugs and biologics for future biomedical treatment. Moreover, the proposed VTAM could also be a general method for other sophisticated polymorphous structures fabrication that isn't achievable by conventional laminar flow.

Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications.

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible movement. However, most existing soft crawling robots typically exhibit a single-motion mode and lack diverse capabilities. Inspired by Drosophila larvae, this paper proposes a compact soft crawling robot (weight, 13 g; length, 165 mm; diameter, 35 mm) with multimodal locomotion (forward, turning, rolling, and twisting). Each robot module uses 4 sets of high-power-density shape memory alloy actuators, endowing it with 4 degrees of motion freedom. We analyze the mechanical characteristics of the robot modules through experiments and simulation analysis. The plug-and-play modules can be quickly assembled to meet different motion and task requirements. The soft crawling robot can be remotely operated with an external controller, showcasing multimodal motion on various material surfaces. In a narrow maze, the robot demonstrates agile movement and effective maneuvering around obstacles. In addition, leveraging the inherent bistable characteristics of the robot modules, we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline detection. The robot completed the inspection in horizontal, vertical, curved, and branched pipelines, adjusted the camera view, and twisted a valve in the pipeline for the first time. Our research highlights the robot's superior locomotion and application capabilities, providing an innovative strategy for the development of lightweight, compact, and multifunctional soft crawling robots.

Insect-Scale Biped Robots Based on Asymmetrical Friction Effect Induced by Magnetic Torque.

Multimodal and controllable locomotion in complex terrain is of great importance for practical applications of insect-scale robots. Robust locomotion plays a particularly critical role. In this study, a locomotion mechanism for magnetic robots based on asymmetrical friction effect induced by magnetic torque is revealed and defined. The defined mechanism overcomes the design constraints imposed by both robot and substrate structures, enabling the realization of multimodal locomotion on complex terrains. Drawing inspiration from human walking and running locomotion, a biped robot based on the mechanism is proposed, which not only exhibits rapid locomotion across substrates with varying friction coefficients but also achieves precise locomotion along patterned trajectories through programmed controlling. Furthermore, apart from its exceptional locomotive capabilities, the biped robot demonstrates remarkable robustness in terms of load-carrying and weight-bearing performance. The presented locomotion and mechanism herein introduce a novel concept for designing magnetic robots while offering extensive possibilities for practical applications in insect-scale robotics.

A Review of Intelligent Walking Support Robots: Aiding Sit-to-Stand Transition and Walking.

Nowadays, numerous countries are facing the challenge of aging population. Additionally, the number of people with reduced mobility due to physical illness is increasing. In response to this issue, robots used for walking assistance and sit-to-stand (STS) transition have been introduced in nursing to assist these individuals with walking. Given the shared characteristics of these robots, this paper collectively refers to them as Walking Support Robots (WSR). Additionally, service robots with assisting functions have been included in the scope of this review. WSR are a crucial element of modern nursing assistants and have received significant research attention. Unlike passive walkers that require much user's strength to move, WSR can autonomously perceive the state of the user and environment, and select appropriate control strategies to assist the user in maintaining balance and movement. This paper offers a comprehensive review of recent literature on WSR, encompassing an analysis of structure design, perception methods, control strategies and safety & comfort features. In conclusion, it summarizes the key findings, current challenges and discusses potential future research directions in this field.

AI co-pilot bronchoscope robot.

The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI-human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.

Learning a simulation-based visual policy for real-world peg in unseen holes.

This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation and adapting to arbitrary unseen shapes in the real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy from the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve a generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN + CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configuration of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2-3 s, using only hundreds of auto-labeled samples for the SN transfer.

DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration.

Pose registration is critical in vision and robotics. This article focuses on the challenging task of initialization-free pose registration up to 7DoF for homogeneous and heterogeneous measurements. While recent learning-based methods show promise using differentiable solvers, they either rely on heuristically defined correspondences or require initialization. Phase correlation seeks solutions in the spectral domain and is correspondence-free and initialization-free. Following this, we propose a differentiable solver and combine it with simple feature extraction networks, namely DPCN++. It can perform registration for homo/hetero inputs and generalizes well on unseen objects. Specifically, the feature extraction networks first learn dense feature grids from a pair of homogeneous/heterogeneous measurements. These feature grids are then transformed into a translation and scale invariant spectrum representation based on Fourier transform and spherical radial aggregation, decoupling translation and scale from rotation. Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step. The entire pipeline is differentiable and trained end-to-end. We evaluate DCPN++ on a wide range of tasks taking different input modalities, including 2D bird's-eye view images, 3D object and scene measurements, and medical images. Experimental results demonstrate that DCPN++ outperforms both classical and learning-based baselines, especially on partially observed and heterogeneous measurements.

Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation.

Common medical image segmentation tasks require large training datasets with pixel-level annotations which are very expensive and time-consuming to prepare. To overcome such limitation and achieve the desired segmentation accuracy, a novel Weakly-Interactive-Mixed Learning (WIML) framework is proposed by efficiently using weak labels. On one hand, utilize weak labels to reduce annotation time for high-quality strong labels by designing a Weakly-Interactive Annotation (WIA) part of the WIML which prudently introduces interactive learning into the weakly-supervised segmentation strategy. On the other hand, utilize weak labels and very few strong labels to achieve desired segmentation accuracy by designing a Mixed-Supervised Learning (MSL) part of the WIML which can boost the segmentation accuracy by providing strong prior knowledge during training. Besides, a multi-task Full-Parameter-Sharing Network (FPSNet) is proposed to better implement this framework. Specifically, to further reduce annotation time, attention modules (scSE) are integrated into FPSNet to improve the class activation map (CAM) performance for the first time. To further improve segmentation accuracy, a Full-Parameter-Sharing (FPS) strategy is designed in FPSNet to alleviate the overfitting of the segmentation task supervised by very few strong labels. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, and experiments demonstrate that the proposed method WIML-FPSNet outperforms several state-of-the-art segmentation methods with minimal annotation efforts.

Unmanned Aerial Vehicle Mediated Drug Delivery for First Aid.

Timely administration of key medications toward patients with sudden diseases is critical to saving lives. However, slow transport of first-aid therapeutics and the potential absence of trained people for drug usage can lead to severe injuries or even death. Herein, an unmanned aerial vehicle (UAV)-mediated first-aid system for targeted delivery (uFAST) is developed. It allows unattended administration of emergency therapeutics-loaded transdermal microneedle (MN) patches toward patients to relieve symptoms by a contact-triggered microneedle applicator (CTMA). The implementability and safety of the uFAST for first aid is demonstrated in a severe hypoglycemic pig model by automatically delivering a glucagon patch with immediate and bioresponsive dual release modes. This platform technique may facilitate the development of UAV-mediated first-aid treatments for other sudden diseases.

Artificial Intelligence-Assisted Bioinformatics, Microneedle, and Diabetic Wound Healing: A "New Deal" of an Old Drug.

Diabetic wounds severely influence life, facing grand challenges in clinical treatments. The demand for better treatment is growing dramatically. Diabetic wound healing is challenging because of inflammation, angiogenesis disruptions, and tissue remodeling. Based on sequencing results of diabetic patients' skins and artificial intelligence (AI)-assisted bioinformatics, we excavate a potential therapeutic agent Trichostatin A (TSA) and a potential target histone deacetylase 4 (HDAC4) for diabetic wound healing. The molecular docking simulation reveals the favorable interaction between TSA and HDAC4. Taking advantage of the microneedle (MN) minimally invasive way to pierce the skin barrier for drug administration, we develop a swelling modified MN-mediated patch loaded with TSA to reduce the probability of injection-caused iatrogenic secondary damage. The MN-mediated TSA patch has been demonstrated to reduce inflammation, promote tissue regeneration, and inhibit HDAC4, which provides superior results in diabetic wound healing. We envisage that our explored specific drug TSA and the related MN-mediated drug delivery system can provide an innovative approach for diabetic wound treatment with simple, effective, and safe features and find a broad spectrum of applications in related biomedical fields.

Milli-scale cellular robots that can reconfigure morphologies and behaviors simultaneously.

Modular robot that can reconfigure architectures and functions has advantages in unpredicted environment and task. However, the construction of modular robot at small-scale remains a challenge since the lack of reliable docking and detaching strategies. Here we report the concept of milli-scale cellular robot (mCEBOT) achieved by the heterogeneous assembly of two types of units (short and long units). Under the magnetic field, the proposed mCEBOT units can not only selectively assemble (e.g., end-by-end and side-by-side) into diverse morphologies corresponding to the unstructured environments, but also configure multi-modes motion behaviors (e.g., slipping, rolling, walking and climbing) based on the on-site task requirements. We demonstrate its adaptive mobility from narrow space to high barrier to wetting surface, and its potential applications in hanging target taking and environment exploration. The concept of mCEBOT offers new opportunities for robot design, and will broaden the field of modular robot in both miniaturization and functionalization.

A Proprioceptive Soft Robot Module Based on Supercoiled Polymer Artificial Muscle Strings.

In this paper, a multi-functional soft robot module that can be used to constitute a variety of soft robots is proposed. The body of the soft robot module made of rubber is in the shape of a long strip, with cylindrical chambers at both the top end and bottom end of the module for the function of actuators and sensors. The soft robot module is driven by supercoiled polymer artificial muscle (SCPAM) strings, which are made from conductive nylon sewing threads. Artificial muscle strings are embedded in the chambers of the module to control its deformation. In addition, SCPAM strings are also used for the robot module's sensing based on the linear relationship between the string's length and their resistance. The bending deformation of the robot is measured by the continuous change of the sensor's resistance during the deformation of the module. Prototypes of an inchworm-like crawling robot and a soft robotic gripper are made, whose crawling ability and grasping ability are tested, respectively. We envision that the proposed proprioceptive soft robot module could potentially be used in other robotic applications, such as continuum robotic arm or underwater robot.

Swarm of micro flying robots in the wild.

Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot.

In this paper, we describe the advances in the design, actuation, modeling, and control field of continuum robots. After decades of pioneering research, many innovative structural design and actuation methods have arisen. Untethered magnetic robots are a good example; its external actuation characteristic allows for miniaturization, and they have gotten a lot of interest from academics. Furthermore, continuum robots with proprioceptive abilities are also studied. In modeling, modeling approaches based on continuum mechanics and geometric shaping hypothesis have made significant progress after years of research. Geometric exact continuum mechanics yields apparent computing efficiency via discrete modeling when combined with numerical analytic methods such that many effective model-based control methods have been realized. In the control, closed-loop and hybrid control methods offer great accuracy and resilience of motion control when combined with sensor feedback information. On the other hand, the advancement of machine learning has made modeling and control of continuum robots easier. The data-driven modeling technique simplifies modeling and improves anti-interference and generalization abilities. This paper discusses the current development and challenges of continuum robots in the above fields and provides prospects for the future.

A 3D-Printed Fin Ray Effect Inspired Soft Robotic Gripper with Force Feedback.

Soft robotic grippers are able to carry out many tasks that traditional rigid-bodied grippers cannot perform but often have many limitations in terms of control and feedback. In this study, a Fin Ray effect inspired soft robotic gripper is proposed with its whole body directly 3D printed using soft material without the need of assembly. As a result, the soft gripper has a light weight, simple structure, is enabled with high compliance and conformability, and is able to grasp objects with arbitrary geometry. A force sensor is embedded in the inner side of the gripper, which allows the contact force required to grip the object to be measured in order to guarantee successful grasping and to provide the most suitable gripping force. In addition, it enables control and data monitoring of the gripper's operating state at all times. Characterization and grasping demonstration of the gripper are given in the Experiment section. Results show that the gripper can be used in a wide range of scenarios and applications, such as the service robot and food industry.

Robot-aided fN∙m torque sensing within an ultrawide dynamic range.

In situ scanning electron microscope (SEM) characterization have enabled the stretching, compression, and bending of micro/nanomaterials and have greatly expanded our understanding of small-scale phenomena. However, as one of the fundamental approaches for material analytics, torsion tests at a small scale remain a major challenge due to the lack of an ultrahigh precise torque sensor and the delicate sample assembly strategy. Herein, we present a microelectromechanical resonant torque sensor with an ultrahigh resolution of up to 4.78 fN∙m within an ultrawide dynamic range of 123 dB. Moreover, we propose a nanorobotic system to realize the precise assembly of microscale specimens with nanoscale positioning accuracy and to conduct repeatable in situ pure torsion tests for the first time. As a demonstration, we characterized the mechanical properties of Si microbeams through torsion tests and found that these microbeams were five-fold stronger than their bulk counterparts. The proposed torsion characterization system pushes the limit of mechanical torsion tests, overcomes the deficiencies in current in situ characterization techniques, and expands our knowledge regarding the behavior of micro/nanomaterials at various loads, which is expected to have significant implications for the eventual development and implementation of materials science.

Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery.

Intra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomography (CT) with annotated target information. Although this technique has been researched for decades, it is still challenging to achieve accuracy, robustness and efficiency simultaneously. In this paper, a novel orthogonal-view 2-D/3-D rigid registration framework is proposed which combines the dense reconstruction based on deep learning and the GPU-accelerated 3-D/3-D rigid registration. First, we employ the X2CT-GAN to reconstruct a target CT from two orthogonal fluoroscopy images. After that, the generated target CT and pre-operative CT are input into the 3-D/3-D rigid registration part, which potentially needs a few iterations to converge the global optima. For further efficiency improvement, we make the 3-D/3-D registration algorithm parallel and apply a GPU to accelerate this part. For evaluation, a novel tool is employed to preprocess the public head CT dataset CQ500 and a CT-DRR dataset is presented as the benchmark. The proposed method achieves 1.65 ± 1.41 mm in mean target registration error(mTRE), 20% in the gross failure rate(GFR) and 1.8 s in running time. Our method outperforms the state-of-the-art methods in most test cases. It is promising to apply the proposed method in localization and nano manipulation of micro surgical robot for highly precise MIS.

Millimeter-Scale Soft Continuum Robots for Large-Angle and High-Precision Manipulation by Hybrid Actuation.

Developing small-scale soft continuum robots with large-angle steering capacity and high-precision manipulation offers broad opportunities in various biomedical settings. However, existing continuum robots reach the bottleneck in actuation on account of the contradiction among small size, compliance actuation, large tender range, high precision, and small dynamic error. Herein, a 3D-printed millimeter-scale soft continuum robot with an ultrathin hollow skeleton wall (300 µm) and a large inner-to-outer ratio (0.8) is reported. After coating a thin ferromagnetic elastomer layer (≈100-150 µm), the proposed soft continuum robot equipped with hybrid actuation (tendon- and magnetic-driven mode) achieves large-angle (up to 100°) steering and high-precision (low to 2 µm for static positioning) micromanipulation simultaneously. Specifically, the robot implements an ultralow dynamic tracking error of ≈10 µm, which is ≈30-fold improved than the state of art. Combined with a microneedle/knife or nasopharyngeal swab, the robot reveals the potential for versatile biomedical applications, such as drug injection on the target tissue, diseased tissue ablation, and COVID-19 nasopharyngeal sampling. The proposed millimeter-scale soft continuum robot presents remarkable advances in large-range and high-precise actuation, which provides a new method for miniature continuum robot design and finds broad applications in biomedical engineering.

An agglutinate magnetic spray transforms inanimate objects into millirobots for biomedical applications.

Millirobots that can adapt to unstructured environments, operate in confined spaces, and interact with a diverse range of objects would be desirable for exploration and biomedical applications. The continued development of millirobots, however, requires simple and scalable fabrication techniques. Here, we propose a minimalist approach to construct millirobots by coating inanimate objects with a composited agglutinate magnetic spray. Our approach enables a variety of one-dimensional (1D), 2D, or 3D objects to be covered with a thin magnetically drivable film (~100 to 250 micrometers in thickness). The film is thin enough to preserve the original size, morphology, and structure of the objects while providing actuation of up to hundreds of times its own weight. Under the actuation of a magnetic field, our millirobots are able to demonstrate a range of locomotive abilities: crawling, walking, and rolling. Moreover, we can reprogram and disintegrate the magnetic film on our millirobots on demand. We leverage these abilities to demonstrate biomedical applications, including catheter navigation and drug delivery.

Battery-Less Soft Millirobot That Can Move, Sense, and Communicate Remotely by Coupling the Magnetic and Piezoelectric Effects.

The soft millirobot is a promising candidate for emerging applications in various in-vivo/vitro biomedical settings. Despite recent success in its design and actuation, the absence of sensing ability makes it still far from being a reality. Here, a radio frequency identification (RFID) based battery-less soft millirobot that can move, sense, and communicate remotely by coupling the magnetic and piezoelectric effects is reported. This design integrates the robot actuation and power generation units within a thin multilayer film (<0.5 mm), i.e., a lower magnetic composite limb decorated with multiple feet imparts locomotion and a flexible piezoceramic composite film recovers energy simultaneously. Under a trigger of external magnetic guidance, the millirobot can achieve remote locomotion, environment monitoring, and wireless communication with no requirement of any on-board battery or external wired power supply. Furthermore, this robot demonstrates the sensing capability in measuring environment temperature and contact interface by two different sensing models, i.e., carried-on and build-in sensing mode, respectively. This research represents a remarkable advance in the emerging area of untethered soft robotics, benefiting a broad spectrum of promising applications, such as in-body monitoring, diagnosis, and drug delivery.