Untethered capsules are capable of entering the gastrointestinal (GI) tract and collecting fluid samples containing microbial communities from specific locations, facilitating the study of chronic diseases. However, existing sampling capsules are designed for single-site sampling, making it challenging to gather samples from multiple targets. This paper reports a magnetic-driven capsule for multiple sampling within the GI tract and an on-demand magnetic-triggered fluid sampling strategy. The capsule consists of a body, a magnetic-triggered negative pressure unit, and a reservoir unit. Composed of an elastic membrane and Magnet I, the negative pressure unit controls pressure change inside the capsule cavity on demand to pump the sample by switching the magnetic field, while the embedded Magnet I also enables real-time magnetic localization for regional targeting and position tracking. The reservoir unit integrates three sampling papers for fluid absorption, two waterproof layers that maintain contamination levels below 25% to ensure reliable multi-site sampling, and a rotating arm embedded with Magnet II for posture adjustment of the sampling paper. The pumping and storage performance of the capsule was systematically evaluated and optimized. Meanwhile, the capsule, actuated by an external magnetic field, was evaluated for its active locomotion performance. Finally, the feasibility of using the capsule to perform active navigation and multi-target sampling in a porcine intestine was validated viaex vivoexperiments.

Microrobot swarms with locomotion dexterity and shape reconfigurability show immense potential in biomedical applications. Automatic control strategies are critical for the navigation of swarms in unstructured environments. Existing control methods mainly focus on initial and final states of swarms, and swarm process control designed to avoid undesired states throughout the control process is yet investigated. In this work, we develop a deep learning-based process control strategy for swarms guided by phase diagrams. Two deep neural networks are respectively built to model the swarm shape and kinematics. Control approaches based on precise swarm models are designed to automatically tune multiple swarm parameters. A phase diagram-based controller is proposed to guide the swarm reconfiguration while eliminating the coupling effects between swarm parameters. The swarm is enabled to accurately track predefined trajectories while performing continuous reconfiguration with desired states during the entire process. By integrating the process control of swarm pattern and locomotion, the swarm can dynamically adapt to constrained unstructured spaces and achieve robust collision avoidance.

Magnetic microswarms have attracted significant attention in medical robotics, owing to their potential for performing complex tasks in challenging environments. However, developing microswarms that can operate effectively in both liquid and air environments remains a substantial challenge. This study presents the design and characterization of hydrogel-based microswarms composed of magnetic hydrogel particles prepared from agarose hydrogel and NdFeB magnetic microparticles. These microswarms form stable monolayer structures actuated by rotating magnetic fields at high frequencies (10 Hz) in liquid environments, enabling synchronization with the external magnet and achieving translational motion. Actuated by an oscillating magnetic field, the swarms transition from a monolayer configuration to a three-dimensional (3D) structure in the air environment. Experimental results demonstrate that the 3D swarms are capable of navigating complex terrains and interacting with tissue surfaces in air environments. Finally, we demonstrate the potential of these 3D swarms for targeted delivery and adaptive filling of gastric perforations using an ex vivo gastric tissue model, showcasing their potential for biomedical applications.

Magnetic microrobots are showing great potential in micromanipulation due to the capability of motion control under external fields. However, achieving selective control of magnetic microrobots in three-dimensional (3D) space using global magnetic fields still presents a challenge. In this work, we propose a selective control strategy based on a movable electromagnetic coil system, incorporating a mass-spring-damping model to achieve precise control of cell microrobots in 3D space. By combining theoretical analysis with vision-based feedback, experiments are demonstrated in different scenarios, including step climbing and ring traversal, validating the control capability in different environments. Furthermore, by utilizing the differences in magnetic responses among cell microrobots, this strategy enables selective manipulation of multiple cell microrobots, demonstrating real-time sorting manipulation in a 3D space. Our work presents a strategy that can be applied to selectively manipulate magnetic microrobots in complex environments.

Obtaining three-dimensional information, especially the z-axis depth information, is crucial for robotic micromanipulation. Due to the unavailability of depth sensors such as lidars in micromanipulation setups, traditional depth acquisition methods such as depth from focus or depth from defocus directly infer depth from microscopic images and suffer from poor resolution. Alternatively, micromanipulation tasks obtain accurate depth information by detecting the contact between an end-effector and an object (e.g., a cell). Despite its high accuracy, only sparse depth data can be obtained due to its low efficiency. This paper aims to address the challenge of acquiring dense depth information during robotic cell micromanipulation. A weakly-supervised depth completion network is proposed to take cell images and sparse depth data obtained by contact detection as input to generate a dense depth map. A two-stage data augmentation method is proposed to augment the sparse depth data, and the depth map is optimized by a network refinement method. The experimental results show that the MAE value of the depth prediction error is less than 0.3 µm, which proves the accuracy and effectiveness of the method. This deep learning network pipeline can be seamlessly integrated with the robotic micromanipulation tasks to provide accurate depth information.

Reconfigurable microswarms have received extensive attention recently. In this work, we propose a control strategy for a ribbon-like swarm to perform navigated locomotion with a stable pattern, and perform controllable splitting into double subswarms to reach two targets simultaneously. Two different behaviors of the ribbon-like swarm are firstly investigated, i.e., locomotion with a stable pattern, and controllable splitting. The two behaviors of the ribbon-like swarm are realized based on different aspect ratio of the swarm. Subsequently, we propose a morphology controller to keep the aspect ratio of the swarm within a desired range. The morphology controller consists of a feedforward controller and a PD controller. The feedforward controller containing a fitted model, and a fuzzy logic controller for online compensation of the model error. The control strategy combining the morphology planning, morphology controller, path planning, and motion controller is developed. Using the proposed control strategy, the ribbon-like swarm can be navigated to follow a desired path with a stable pattern while avoiding obstacles, and finally perform controllable splitting into double subswarms to reach two predefined targets simultaneously.

Miniature robots capable of untethered operation hold great promise for performing diagnostic and therapeutic procedures in hard-to-reach regions within the human body. Nonetheless, navigating these complex and diverse physiological environments remains a significant challenge. To effectively navigate the tortuous pathways inside the human body, it is essential to equip miniature robots with flexible body structures that can adapt to complex geometries and develop efficient actuation strategies for deformed robots. In this study, we present a miniature soft robot featuring a zigzag body structure, imparting the robot with remarkable deformation capabilities that enable it to adapt to confined and tortuous spaces. This robot is equipped with arrays of magnetic legs, enabling robust locomotion propelled by traveling metachronal waves. We demonstrate that the robot can crawl on both flat surfaces and slopes. Leveraging its in-plane flexibility and discrete actuation system, this robot can navigate through intricate environments with precise control using magnetic fields. Our work provides valuable insights into the development of crawling robots with enhanced agility and adaptability, creating opportunities for their future use in a wide range of biomedical applications.

Magnetic field-driven microrobotic swarms have drawn extensive attention, especially in the field of automatic control. Realizing dynamic path planning and motion control of microrobotic swarms for mobile target tracking is one of the important tasks that still remains unsolved. In this paper, we firstly present an enhanced bidirectional rapidly-exploring random tree star (EB-RRT*) algorithm considering the physical size of the swarm to dynamically plan the optimal path for obstacle avoidance. An image-guided motion controller, which consists of a direction controller and a Genetic Algorithm based Linear Quadratic Regulator (GA-LQR) velocity controller, is then proposed to realize mobile target tracking using microrobotic swarms. Targeted bursting algorithm is subsequently developed to meet the requirement of tracking high-speed (i.e., 20$\mu m/s$) mobile targets. Simulations are performed to validate the proposed methods and obtain the proper ranges of the input parameters for the controllers. Finally, the control effectiveness of mobile target tracking in different conditions and environments is validated by experimental results. Note to Practitioners—The motivation of this work is to develop an effective control scheme for mobile target tracking using microrobotic swarms. Conventional control schemes mainly focus on the control of single microrobots to reach static targets, and thus the desired path is fixed once planned. In addition, the motion of single monolithic microrobots can be modelled precisely. However, in mobile target tracking using microrobotic swarms, dynamic planning algorithms are demanded to frequently update the desired path. Swarms consisting of millions of micro-agents are also difficult to be modelled due to the complex agent-agent interactions. In this work, an effective control scheme consisting of a dynamic path planner, a motion controller and a targeted bursting unit is developed. Real-time dynamic paths will be planned even though the positions of the swarm and the target change rapidly. The precise control of the swarm direction and velocity are achieved, and moreover, using the targeted bursting algorithm, the swarm can be accelerated to approach mobile targets accurately with higher efficiency. Experimental results validates the proposed tracking strategy in different environments with virtual obstacles. The proposed control scheme paves the way for a better understanding of advanced motion control methods for microrobotic swarms.

Magnetic miniature robots are promising tools for minimally invasive and noninvasive therapy. Constructing systems with actuation–perception loops is an essential step to progress from fundamental research to clinical applications, and from manual to automated manipulation. Such systems include imaging devices for tracking miniature robots inside a living body, and magnetic actuators for manipulating the robots. In this survey article, the designs, features, and control of various magnetic actuation systems with imaging modalities are summarized. The strategies of actuation–perception cooperation are discussed from both hardware and software aspects, aiming to provide a paradigm for building automated image-guided systems in clinical scenarios. Furthermore, the solutions when both the systems and surgeons simultaneously participate in the operation are introduced. We also discuss the advantages and drawbacks of reported techniques, major challenges, and potential prospects in this field.

Control and navigation of microrobotic swarms have drawn extensive attention recently. Avoiding dynamic obstacles using swarms is one of the major challenges that still remain unsolved. In this work, we develop a control strategy to navigate microrobotic swarms to targeted positions while avoiding dynamic obstacles. We first propose a criterion to evaluate the real-time locomotion efficiency during dynamic obstacle avoidance, i.e., the swarm moving direction and the distance between the swarm and the target. Subsequently, a hierarchical radar with three functional boundaries (detection, safety, and prediction circle) is designed for swarms. The optimal moving direction of the swarm with the existence of dynamic obstacles is selected based on the three circles. The effectiveness of the algorithm is validated by simulations and experiments. Using the proposed strategy, the swarm is capable of avoiding multiple moving obstacles and reaching the predefined target. Finally, to show the compatibility of the proposed control method, the swarm is deployed in a micromaze with different dynamic obstacles, and the results also validate the effectiveness of the strategy.

The small size and wireless actuation of microrobots make them potential candidates for minimally invasive medicine. To advance microrobots to future clinical application, microrobotics researchers have investigated a number of key issues, in which swarm control is a primary challenge and is attracting increasing attention. As a single microrobot has limited volume and surface area, clinically relevant tasks, includingin-vivotracking, usually require simultaneous control of a large swarm of microrobots. Unlike macroscale robots, implementing on-board actuators and sensors for microrobots is challenging, which differentiates swarm microrobotics from other swarm robotics approaches. This article systematically summarizes the state of the art for this emerging field, including actuation systems with different power sources, swarm behaviors modeling and simulation, swarm control strategies, and targeted biomedical applications. Actuation principles of microrobot swarms are categorized in detail, and critical comparisons are made to provide guidance and insight for future swarm microrobotics researchers. Considering the unique features of swarm microrobotics compared to traditional swarm robotics, this article also emphasizes the modeling, simulation, and control of microrobot swarms. Furthermore, recent biomedical applications of microrobot swarms are summarized to illustrate specific application scenarios. Finally, we provide an assessment of the future directions of swarm microrobotics.

Reconfigurable microrobotic swarms and controllable active matter systems have drawn extensive attention recently. Developing effective actuation strategies and control schemes that enable embodied intelligence of microscopic swarms are both major challenges. In this work, we realize the generation of an elliptical paramagnetic nanoparticle swarm (EPNS) with enhanced dexterity for adaptive locomotion, and subsequently a fuzzy control strategy is developed for automatically tuning pattern deformation, orientation, and position of the swarm. By adjusting the input field, the aspect ratio of the EPNS will change accordingly, and we demonstrate its adaptive navigation through curved and narrowed channel by performing pattern reconfigurations. Moreover, using the proposed control strategy, precise matches can be reached between the controlled swarms and the desired patterns. Finally, to show the high compatibility of the control strategy, we employ ribbon-like colloidal swarms driven by oscillating magnetic field, and the results also validate the effectiveness of the strategy.

We propose thrombolysis using a magnetic nanoparticle microswarm with tissue plasminogen activator (tPA) under ultrasound imaging. The microswarm is generated in blood using an oscillating magnetic field and can be navigated with locomotion along both the long and short axis. By modulating the input field, the aspect ratio of the microswarm can be reversibly tuned, showing the ability to adapt to different confined environments. Simulation results indicate that both in-plane and out-of-plane fluid convection are induced around the microswarm, which can be further enhanced by tuning the aspect ratio of the microswarm. Under ultrasound imaging, the microswarm is navigated in a microchannel towards a blood clot and deformed to obtain optimal lysis. Experimental results show that the lysis rate reaches -0.1725 ± 0.0612 mm3/min in the 37°C blood environment under the influence of the microswarm-induced fluid convection and tPA. The lysis rate is enhanced 2.5-fold compared to that without the microswarm (-0.0681 ± 0.0263 mm3/min). Our method provides a new strategy to increase the efficiency of thrombolysis by applying microswarm-induced fluid convection, indicating that swarming micro/nanorobots have the potential to act as effective tools towards targeted therapy.

Recently, swarm control of micro-/nanorobots has drawn much attention in the field of microrobotics. This paper reports a mobile paramagnetic nanoparticle swarm with the capability of active shape deformation that can improve its environment adaptability. We show that, by applying elliptical rotating magnetic fields, a swarm pattern called the elliptical paramagnetic nanoparticle swarm (EPNS) would be formed. When changing the field ratio-α (i.e. the strength ratio between the minor axis and major axis of the elliptical field), the shape ratio-β of the EPNS (i.e. the length ratio between the major axis and minor axis) will change accordingly. However, automatically control this shape deformation process has difficulties because the deformation dynamics has strong nonlinearity, model variation and long time requirement. To solve this problem, we propose a fuzzy logic-based control scheme that utilizes the knowledge and control experience from skilled human operators. Experiments show that the proposed control scheme can stably maneuver the shape deformation of the EPNS with small overshoot, which cannot be achieved by conventional PI control. Moreover, experimental results show that, with the automatic shape deformation control, shape of the EPNS is controlled with high reversibility and also can be well maintained during the planar rotational and translational locomotion of the EPNS.

Swarm control is one of the primary challenges in microrobotics. For the automated control of such a microrobotic system with small size and large population, conventional methods using precise robot models and robot-robot communications lose effectiveness due to the complex locomotion of micro/nano agents in a swarm and difficult implementation of onboard actuators and sensors for individual motion control and motion feedback. This article proposes a statistics-based approach and reports the fully automated control of a swarm of paramagnetic nanoparticles including the swarm pattern formation, identification, tracking, motion control, and real-time distribution monitoring/control. By establishing the swarm statistics, collective behaviors of a nanoparticle swarm can be quantitatively analyzed by computers. Algorithms are designed based on the statistics to automatically generate and identify the vortex-like paramagnetic nanoparticle swarm (VPNS), which present robustness to the dose and initial distribution of the nanoparticle swarm. In order to robustly track a VPNS, a statistics-based tracking method is proposed, in which 500 boundary points of the VPNS are extracted and the VPNS distribution is optimally recognized. And, with the proposed gathering improvement control, experiments show that over 70% nanoparticles can be gathered in the VPNS. Furthermore, an automated motion control scheme for the VPNS is proposed which shows high-accuracy trajectory tracking performance (tracking error: <; 5% body length). Besides, real-time monitoring of the distribution region/density and control of the distribution area for a nanoparticle swarm are also realized by using the statistics. Experimental results validate the feasibility of the proposed method in automated control of paramagnetic nanoparticle swarms.

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle-based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.

Microrobots are considered as potential candidates for targeted delivery of cargos, drugs and even energy with high precision. One interesting phenomenon is their collective behaviour actuated by dynamic fields, which is yet to be adequately studied. Herein, we report a novel method of using millions of magnetic nanoparticles to generate a dynamic-equilibrium particle-based vortex, which can manipulate multiple cargos simultaneously at the microscale. The governing physical laws of the generation of a particle-based vortex are explained and the experimental results are presented. The high effectiveness of this micro-vortex-based method of particle gathering is testified. Moreover, the vortex can be navigated near a solid surface in a controlled manner. The velocity and morphology of the mobile vortices with different pitch angles are investigated, showing that the vortex moving with small pitch angles is capable of maintaining the original shape and coverage area. Collecting and transporting multiple polystyrene (PS) microbeads into a channel using the vortex are also demonstrated. This method allows us to perform micromanipulation using the collective behaviour of nanoparticles and to develop new strategies for the formation and control of the microrobotic swarm.

Paramagnetic nanoparticles are considered as attractive building blocks, particularly for robotic delivery of drugs. Although paramagnetic nanoparticles can be effectively gathered and transported using external magnetic fields, the disassembly process is yet to be fully investigated to avoid the formation of aggregations. In this paper, we report a novel method of controllable disassembly of paramagnetic nanoparticle chains using a predefined dynamic magnetic field. The dynamic field is capable of performing spreading and fragmentation of the particle chains simultaneously. Using the magnetic dipole-dipole repulsive forces, the final area covered by the particle chains swells up to 545% of the initial area. The final length distribution presents a strong relationship with the frequency of the dynamic field in deionized (DI) water and two kinds of biofluids. An analytical model of phase lag is proposed, which shows good agreement with the experimental results. Furthermore, we also present an assembly process using a rotating magnetic field, indicating that the assembly disassembly process is reversible. In addition, batch-cargo delivery of polystyrene microbeads using the nanoparticle chains as swarm-like nanorobots is demonstrated.

We present a general solution for steering microrobotic swarm by dynamic actuating fields. In our approach, the motion of micro-robots is controlled by changing the actuating direction of a field applied to them. The time-series sequence of actuating field's directions can be computed automatically. Given a target position in the domain of swarm, a governing field is first constructed to provide optimal moving directions at every points. Following these directions, a robot can be driven to the target efficiently. However, when working with a crowd of micro-robots, the optimal moving directions on different agents can contradict with each other. To overcome this difficulty, we develop a novel steering algorithm to compute a statistically optimal actuating direction at each time frame. Following a sequence of these actuating directions, a crowd of micro-robots can be transported to the target region effectively. Our steering strategy of swarm has been verified on a platform that generates magnetic fields with unique actuating directions. Experimental tests taken on aggregated magnetic micro-particles are quite encouraging.